如何通过正则表达式过滤熊猫中的行

I would like to cleanly filter a dataframe using regex on one of the columns.

For a contrived example:

In [210]: foo = pd.DataFrame({'a' : [1,2,3,4], 'b' : ['hi', 'foo', 'fat', 'cat']})
In [211]: foo
Out[211]: 
   a    b
0  1   hi
1  2  foo
2  3  fat
3  4  cat

I want to filter the rows to those that start with f using a regex. First go:

In [213]: foo.b.str.match('f.*')
Out[213]: 
0    []
1    ()
2    ()
3    []

That’s not too terribly useful. However this will get me my boolean index:

In [226]: foo.b.str.match('(f.*)').str.len() > 0
Out[226]: 
0    False
1     True
2     True
3    False
Name: b

So I could then do my restriction by:

In [229]: foo[foo.b.str.match('(f.*)').str.len() > 0]
Out[229]: 
   a    b
1  2  foo
2  3  fat

That makes me artificially put a group into the regex though, and seems like maybe not the clean way to go. Is there a better way to do this?

Use contains instead:

In [10]: df.b.str.contains('^f')
Out[10]: 
0    False
1     True
2     True
3    False
Name: b, dtype: bool

There is already a string handling function Series.str.startswith(). You should try foo[foo.b.str.startswith('f')].

Result:

    a   b
1   2   foo
2   3   fat

I think what you expect.

Alternatively you can use contains with regex option. For example:

foo[foo.b.str.contains('oo', regex= True, na=False)]

Result:

    a   b
1   2   foo

na=False is to prevent Errors in case there is nan, null etc. values

Multiple column search with dataframe:

frame[frame.filename.str.match('*.'+MetaData+'.*') & frame.file_path.str.match('C:\test\test.txt')]

It may be a bit late, but this is now easier to do in Pandas by calling . The docs explain the difference between match, fullmatch and contains.

Note that in order to use the results for indexing, set the na=False argument (or True if you want to include NANs in the results).

Thanks for the great answer @user3136169, here is an example of how that might be done also removing NoneType values.

def regex_filter(val):
    if val:
        mo = re.search(regex,val)
        if mo:
            return True
        else:
            return False
    else:
        return False

df_filtered = df[df['col'].apply(regex_filter)]

Also you can also add regex as an arg:

def regex_filter(val,myregex):
    ...

df_filtered = df[df['col'].apply(res_regex_filter,regex=myregex)]

Write a Boolean function that checks the regex and use apply on the column

foo[foo['b'].apply(regex_function)]

Using str slice

foo[foo.b.str[0]=='f']
Out[18]: 
   a    b
1  2  foo
2  3  fat