根据正则表达式过滤数据帧

Say I have a dataframe my_df with a column 'brand', I would like to drop any rows where brand is either toyota or bmw.

假设我有一个带有'品牌'列的数据框my_df,我想放弃任何品牌是丰田或宝马的行。

I thought the following would do it:

我认为以下会这样做:

my_regex = re.compile('^(bmw$|toyota$).*$')
my_function = lambda x: my_regex.match(x.lower())
my_df[~df['brand'].apply(my_function)] 

but I get the error:

但我得到错误:

ValueError: cannot index with vector containing NA / NaN values

Why? How can I filter my DataFrame using a regex?

为什么?如何使用正则表达式过滤我的DataFrame?

1 个解决方案

#1

I think re.match returns None when there is no match and that breaks the indexing; below is an alternative solution using pandas vectorized string methods; note that pandas string methods can handle null values:

我认为re.match在没有匹配时返回None并且会破坏索引;下面是使用pandas矢量化字符串方法的替代解决方案;请注意,pandas字符串方法可以处理空值:

>>> df = pd.DataFrame( {'brand':['BMW', 'FORD', np.nan, None, 'TOYOTA', 'AUDI']})
>>> df
    brand
0     BMW
1    FORD
2     NaN
3    None
4  TOYOTA
5    AUDI

[6 rows x 1 columns]

>>> idx = df.brand.str.contains('^bmw$|^toyota$', 
             flags=re.IGNORECASE, regex=True, na=False)
>>> idx
0     True
1    False
2    False
3    False
4     True
5    False
Name: brand, dtype: bool

>>> df[~idx]
  brand
1  FORD
2   NaN
3  None
5  AUDI

[4 rows x 1 columns]