根据正则表达式字典填充Pandas DataFrame列 [英] Populating Pandas DataFrame column based on dictionary of regex

问题描述

我有一个如下数据框:

    GE    GO
1   AD    Weiss
2   KI    Ruby
3   OH    Port
4   ER    Rose
5   KI    Rose
6   JJ    Weiss
7   OH    7UP
8   AD    7UP
9   OP    Coke
10  JJ    Stout

，并且我尝试根据列GO的值再添加一列.我当时正在考虑使用字典，但是我需要使用正则表达式来识别部分真实情况.例如:

and I'm trying to add one more column based on the value of of column GO. I was thinking about using a dictionary, but I need to use regex to identify partial matches in my real case. For instance:

Dic={'Weiss|\wuby|Sto\w+':'Beer', 'Port|Rose':'Wine','\dUP|Coke':'Soda'}

这会给

    GE    GO    OUT
1   AD    Weiss Beer
2   KI    Ruby  Beer
3   OH    Port  Wine
4   ER    Rose  Wine
5   KI    Rose  Wine
6   JJ    Weiss Beer
7   OH    7UP   Soda
8   AD    7UP   Soda
9   OP    Coke  Soda
10  JJ    Stout Beer

lambda函数在这里可以工作吗?我将如何使其成为正则表达式?预先感谢！

Would a lambda function work here? How would I make it into regex? Thanks in advance!

推荐答案

您可以这样做:

In [253]: df['OUT'] = df[['GO']].replace({'GO':Dic}, regex=True)

In [254]: df
Out[254]:
    GE     GO   OUT
1   AD  Weiss  Beer
2   KI   Ruby  Beer
3   OH   Port  Wine
4   ER   Rose  Wine
5   KI   Rose  Wine
6   JJ  Weiss  Beer
7   OH    7UP  Soda
8   AD    7UP  Soda
9   OP   Coke  Soda
10  JJ  Stout  Beer

有趣的观察-在较早的Pandas版本中，与DataFrame.replace()和Series.str.replace()方法相比，Series.map()方法几乎总是更快.在Pandas 0.19.2中变得更好:

Intereseting observation - in older Pandas versions, Series.map() method was almost always faster compared to DataFrame.replace() and Series.str.replace() methods. It got better in Pandas 0.19.2:

In [267]: df = pd.concat([df] * 10**4, ignore_index=True)

In [268]: %timeit df.GO.map(lambda x: next(Dic[k] for k in Dic if re.search(k, x)))
1 loop, best of 3: 1.57 s per loop

In [269]: %timeit df[['GO']].replace({'GO':Dic}, regex=True)
1 loop, best of 3: 895 ms per loop

In [270]: %timeit df.GO.replace(Dic, regex=True)
1 loop, best of 3: 876 ms per loop

In [271]: df.shape
Out[271]: (100000, 2)

这篇关于根据正则表达式字典填充Pandas DataFrame列的文章就介绍到这了，希望我们推荐的答案对大家有所帮助，也希望大家多多支持IT屋！