按整数索引选择一行pandas系列/ dataframe [英] Selecting a row of pandas series/dataframe by integer index
问题描述
我很好奇为什么不支持 df [2]
,而 df.ix [2]
和 df [2:3]
都有效。
I am curious as to why df[2]
is not supported, while df.ix[2]
and df[2:3]
both work.
In [26]: df.ix[2]
Out[26]:
A 1.027680
B 1.514210
C -1.466963
D -0.162339
Name: 2000-01-03 00:00:00
In [27]: df[2:3]
Out[27]:
A B C D
2000-01-03 1.02768 1.51421 -1.466963 -0.162339
我希望 df [2]
以与 df [2:3]相同的方式工作]
与Python索引约定保持一致。是否有设计原因不支持索引行的单个整数?
I would expect df[2]
to work the same way as df[2:3]
to be consistent with Python indexing convention. Is there a design reason for not supporting indexing row by single integer?
推荐答案
回显@HYRY,请参阅0.11中的新文档
echoing @HYRY, see the new docs in 0.11
http:// pandas .pydata.org / pandas-docs / stable / indexing.html
这里我们有新的运营商, .iloc
表示只支持整数索引, .loc
表示只支持标签索引
Here we have new operators, .iloc
to explicity support only integer indexing, and .loc
to explicity support only label indexing
例如想象一下这个场景
In [1]: df = pd.DataFrame(np.random.rand(5,2),index=range(0,10,2),columns=list('AB'))
In [2]: df
Out[2]:
A B
0 1.068932 -0.794307
2 -0.470056 1.192211
4 -0.284561 0.756029
6 1.037563 -0.267820
8 -0.538478 -0.800654
In [5]: df.iloc[[2]]
Out[5]:
A B
4 -0.284561 0.756029
In [6]: df.loc[[2]]
Out[6]:
A B
2 -0.470056 1.192211
[]
仅对行进行切片(按标签位置)
[]
slices the rows (by label location) only
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