pandas 使用startswith从Dataframe中选择 [英] pandas select from Dataframe using startswith

问题描述

这有效(使用Pandas 12 dev)

This works (using Pandas 12 dev)

table2=table[table['SUBDIVISION'] =='INVERNESS']

然后我意识到我需要使用开头为"来选择字段，因为我缺少一堆. 因此，根据我所能追踪的Pandas文档，我尝试过

Then I realized I needed to select the field using "starts with" Since I was missing a bunch. So per the Pandas doc as near as I could follow I tried

criteria = table['SUBDIVISION'].map(lambda x: x.startswith('INVERNESS'))
table2 = table[criteria]

并得到AttributeError:'float'对象没有属性'startswith'

And got AttributeError: 'float' object has no attribute 'startswith'

所以我尝试了具有相同结果的替代语法

So I tried an alternate syntax with the same result

table[[x.startswith('INVERNESS') for x in table['SUBDIVISION']]]

参考 http://pandas.pydata.org/pandas-docs/stable/indexing.html#boolean-indexing 第4节:系列的列表理解和映射方法也可以用于生成更复杂的条件:

Reference http://pandas.pydata.org/pandas-docs/stable/indexing.html#boolean-indexing Section 4: List comprehensions and map method of Series can also be used to produce more complex criteria:

我想念什么?

推荐答案

您可以使用 str.startswith DataFrame方法可提供更一致的结果:

You can use the str.startswith DataFrame method to give more consistent results:

In [11]: s = pd.Series(['a', 'ab', 'c', 11, np.nan])

In [12]: s
Out[12]:
0      a
1     ab
2      c
3     11
4    NaN
dtype: object

In [13]: s.str.startswith('a', na=False)
Out[13]:
0     True
1     True
2    False
3    False
4    False
dtype: bool

和布尔索引将正常工作(我更喜欢使用loc，但不使用它的情况相同):

and the boolean indexing will work just fine (I prefer to use loc, but it works just the same without):

In [14]: s.loc[s.str.startswith('a', na=False)]
Out[14]:
0     a
1    ab
dtype: object

.

看起来在Series/列中您的至少一个元素是浮点数，它没有startswith方法，因此AttributeError，列表推导应该引发相同的错误...

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