大 pandas :按二级索引范围对MultiIndex进行切片 [英] pandas: slice a MultiIndex by range of secondary index
问题描述
我有一个这样的具有MultiIndex的系列:
I have a series with a MultiIndex like this:
import numpy as np
import pandas as pd
buckets = np.repeat(['a','b','c'], [3,5,1])
sequence = [0,1,5,0,1,2,4,50,0]
s = pd.Series(
np.random.randn(len(sequence)),
index=pd.MultiIndex.from_tuples(zip(buckets, sequence))
)
# In [6]: s
# Out[6]:
# a 0 -1.106047
# 1 1.665214
# 5 0.279190
# b 0 0.326364
# 1 0.900439
# 2 -0.653940
# 4 0.082270
# 50 -0.255482
# c 0 -0.091730
我想获取第二个索引('sequence
')在2到10之间的s ['b']值.
I'd like to get the s['b'] values where the second index ('sequence
') is between 2 and 10.
在第一个索引上切片可以很好地工作:
Slicing on the first index works fine:
s['a':'b']
# Out[109]:
# bucket value
# a 0 1.828176
# 1 0.160496
# 5 0.401985
# b 0 -1.514268
# 1 -0.973915
# 2 1.285553
# 4 -0.194625
# 5 -0.144112
但不是第二种,至少在看来最明显的两种方式上是这样的:
But not on the second, at least by what seems to be the two most obvious ways:
1)这将返回元素1至4,与索引值无关
1) This returns elements 1 through 4, with nothing to do with the index values
s['b'][1:10]
# In [61]: s['b'][1:10]
# Out[61]:
# 1 0.900439
# 2 -0.653940
# 4 0.082270
# 50 -0.255482
但是,如果我反转索引,并且第一个索引是整数,第二个索引是字符串,则它可以工作:
However, if I reverse the index and the first index is integer and the second index is a string, it works:
In [26]: s
Out[26]:
0 a -0.126299
1 a 1.810928
5 a 0.571873
0 b -0.116108
1 b -0.712184
2 b -1.771264
4 b 0.148961
50 b 0.089683
0 c -0.582578
In [25]: s[0]['a':'b']
Out[25]:
a -0.126299
b -0.116108
推荐答案
为 Robbie-Clarken答案,因为您是0.14可以在传递给loc的元组中传递切片 :
As Robbie-Clarken answers, since 0.14 you can pass a slice in the tuple you pass to loc:
In [11]: s.loc[('b', slice(2, 10))]
Out[11]:
b 2 -0.65394
4 0.08227
dtype: float64
实际上,您可以为每个级别传递一个切片:
Indeed, you can pass a slice for each level:
In [12]: s.loc[(slice('a', 'b'), slice(2, 10))]
Out[12]:
a 5 0.27919
b 2 -0.65394
4 0.08227
dtype: float64
注意:切片包含在内.
您也可以使用:
s.ix[1:10, "b"]
(由于此版本允许赋值,因此最好在一个ix/loc/iloc中执行此操作.)
(It's good practice to do in a single ix/loc/iloc since this version allows assignment.)
此答案写在介绍iloc 之前,即位置/整数位置-在这种情况下可能是首选.创建它的原因是为了消除整数索引的熊猫对象的歧义,并且更具描述性:我在位置上切片".
This answer was written prior to the introduction of iloc in early 2013, i.e. position/integer location - which may be preferred in this case. The reason it was created was to remove the ambiguity from integer-indexed pandas objects, and be more descriptive: "I'm slicing on position".
s["b"].iloc[1:10]
也就是说,我有点不同意ix是的文档:
That said, I kinda disagree with the docs that ix is:
最健壮和一致的方式
most robust and consistent way
不是,最一致的方法是描述您在做什么:
it's not, the most consistent way is to describe what you're doing:
- 使用loc作为标签
- 使用iloc定位
- 同时使用ix(如果确实需要)
记住禅宗的python :
显式优于隐式
explicit is better than implicit
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