获取 pandas 中某个索引值之前和之后的行数 [英] Get number of rows before and after a certain index value in pandas
问题描述
假设我有以下内容:
In [1]: import pandas as pd
import numpy as np
df = pd.DataFrame(data=np.random.rand(11),index=pd.date_range('2015-04-20','2015-04-30'),columns=['A'])
Out[1]:
A
2015-04-20 0.694983
2015-04-21 0.393851
2015-04-22 0.690138
2015-04-23 0.674222
2015-04-24 0.763175
2015-04-25 0.761917
2015-04-26 0.999274
2015-04-27 0.907871
2015-04-28 0.464818
2015-04-29 0.005733
2015-04-30 0.806351
我有一些复杂的方法将单个索引标识为有趣的,例如'2015-04-25'.我可以使用以下方式检索具有该索引的行:
I have some complicated method that identifies a single index as being interesting, for example '2015-04-25'. I can retrieve the row with that index using:
In [2]: df.loc['2015-04-25']
Out[2]:
A 0.761917
Name: 2015-04-25 00:00:00, dtype: float64
获取该索引值之前和/或之后的n行数的最佳方法是什么?
What would be the nicest way to obtain a number of n rows before and/or after that index value?
我想做的事情是这样的:
What I would like to do is something like:
In[3]: df.getRowsBeforeLoc('2015-04-25',3)
Out[3]:
2015-04-22 0.690138
2015-04-23 0.674222
2015-04-24 0.763175
2015-04-25 0.761917
或等效地:
In[3]: df.getRowsAfterLoc('2015-04-25',3)
Out[3]:
2015-04-25 0.761917
2015-04-26 0.999274
2015-04-27 0.907871
2015-04-28 0.464818
(对于是否包含与目标索引值本身相对应的行,我没有很强的看法.)
(I don't have a strong opinion on whether or not the row that corresponds to the target index value itself is included.)
推荐答案
loc
支持切片范围内的乞讨/结束点:
loc
supports slicing the beg/end point is included in the range:
In [363]:
df.loc[:'2015-04-25']
Out[363]:
A
2015-04-25 0.141787
2015-04-26 0.598237
2015-04-27 0.106461
2015-04-28 0.297159
2015-04-29 0.058392
2015-04-30 0.621325
In [364]:
df.loc['2015-04-25':]
Out[364]:
A
2015-04-25 0.141787
2015-04-26 0.598237
2015-04-27 0.106461
2015-04-28 0.297159
2015-04-29 0.058392
2015-04-30 0.621325
要获取前(n)行,请使用 tail
:
To get either first/last (n) rows use head
/tail
:
In [378]:
df.loc[:'2015-04-25'].head(3)
Out[378]:
A
2015-04-20 0.827699
2015-04-21 0.901140
2015-04-22 0.427304
In [377]:
df.loc[:'2015-04-25'].tail(3)
Out[377]:
A
2015-04-23 0.002189
2015-04-24 0.041965
2015-04-25 0.141787
更新
To get the row before/after a specifc value we can use get_loc
on the index to return an integer position and then use this with iloc
to get the previous/next row:
In [388]:
df.index.get_loc('2015-04-25')
Out[388]:
5
In [391]:
df.iloc[df.index.get_loc('2015-04-25')-1]
Out[391]:
A 0.041965
Name: 2015-04-24 00:00:00, dtype: float64
In [392]:
df.iloc[df.index.get_loc('2015-04-25')+1]
Out[392]:
A 0.598237
Name: 2015-04-26 00:00:00, dtype: float64
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