熊猫数据帧过滤器正则表达式

Question

I don't understand pandas DataFrame filter .

Setup

import pandas as pd

df = pd.DataFrame(
    [
        ['Hello', 'World'],
        ['Just', 'Wanted'],
        ['To', 'Say'],
        ['I\'m', 'Tired']
    ]
)

Problem

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=0 to specify filtering rows. What I get is this:

       0      1
0  Hello  World

I was expecting

       0       1
0  Hello   World
1   Just  Wanted

Question

• What would have gotten me what I expected?

Answer 1

Per the docs ,

Arguments are mutually exclusive, but this is not checked for

So, it appears, the first optional argument, items=[0] trumps the third optional argument, 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

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.

---

To get the desired result, you could use str.contains to create a boolean mask, and use df.loc to select rows:

In [210]: df.loc[df.iloc[:,0].str.contains(r'(Hel|Just)')]
Out[210]: 
       0       1
0  Hello   World
1   Just  Wanted

Answer 2

这应该有效：

df[df[0].str.contains('(Hel|Just)', regex=True)]

Answer 3

这是一个链接方法：

df.loc[lambda x: x['column_name'].str.contains(regex_patern, regex = True)]