Python 熊猫数据帧过滤器正则表达式
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pandas DataFrame filter regex
提问by piRSquared
I don't understand pandasDataFramefilter.
我不明白pandasDataFramefilter。
Setup
设置
import pandas as pd
df = pd.DataFrame(
[
['Hello', 'World'],
['Just', 'Wanted'],
['To', 'Say'],
['I\'m', 'Tired']
]
)
Problem
问题
df.filter([0], regex=r'(Hel|Just)', axis=0)
I'd expect the [0]to specify the 1st column as the one to look at and axis=0to specify filtering rows. What I get is this:
我希望[0]将第一列指定为要查看和axis=0指定过滤行的列。我得到的是这样的：
0 1
0 Hello World
I was expecting
我期待
0 1
0 Hello World
1 Just Wanted
Question
题
- What would have gotten me what I expected?
- 什么会让我达到我的预期？
回答by unutbu
Per the docs,
根据文档，
Arguments are mutually exclusive, but this is not checked for
参数是互斥的，但这不会被检查
So, it appears, the first optional argument, items=[0]trumps the third optional argument, regex=r'(Hel|Just)'.
因此，似乎第一个可选参数items=[0]胜过第三个可选参数regex=r'(Hel|Just)'。
In [194]: df.filter([0], regex=r'(Hel|Just)', axis=0)
Out[194]:
0 1
0 Hello World
is equivalent to
相当于
In [201]: df.filter([0], axis=0)
Out[201]:
0 1
0 Hello World
which is merely selecting the row(s) with index values in [0]along the 0-axis.
这只是选择[0]沿 0 轴具有索引值的行。
To get the desired result, you could use str.containsto create a boolean mask,
and use df.locto select rows:
要获得所需的结果，您可以使用str.contains创建一个布尔掩码，并用于df.loc选择行：
In [210]: df.loc[df.iloc[:,0].str.contains(r'(Hel|Just)')]
Out[210]:
0 1
0 Hello World
1 Just Wanted
回答by Max
This should work:
这应该有效：
df[df[0].str.contains('(Hel|Just)', regex=True)]
df[df[0].str.contains('(Hel|Just)', regex=True)]
回答by Ramin Melikov
Here is a chaining method:
这是一个链接方法：
df.loc[lambda x: x['column_name'].str.contains(regex_patern, regex = True)]
