python – Pandas数据帧,按最后一列的最后一列拆分数据,但保留其他列

对熊猫来说很新,所以对@R_502_6280@案的任何解释都表示赞赏.

我有一个数据帧,如

    Company                             Zip State City
1   *CBRE                               San Diego, CA 92101
4   1908 Brands                         Boulder, CO 80301
7   1st Infantry Division Headquarters  Fort Riley, KS
10  21st Century Healthcare, Inc.       Tempe 85282
15  AAA                                 Jefferson City, MO 65101-9564

我想将我的数据中的Zip State city列拆分为3个不同的列.使用Pandas DataFrame, how do i split a column into two这篇文章的答案,如果我没有第一列,我就可以完成这项任务.编写正则表达式来捕获所有公司只会导致我捕获数据中的所有内容.

我也试过了

foo = lambda x: pandas.Series([i for i in reversed(x.split())])
data_pretty = data['Zip State City'].apply(foo)

但这导致我松开公司列并将多个单词的城市名称拆分为不同的列.

如何在保留公司列数据的同时拆分我的最后一列？

解决方法:

你可以使用extract()方法：

In [110]: df
Out[110]:
                               Company                 Zip State City
1                                *CBRE            San Diego, CA 92101
4                          1908 Brands              Boulder, CO 80301
7   1st Infantry Division Headquarters                 Fort Riley, KS
10       21st Century Healthcare, Inc.                    Tempe 85282
15                                 AAA  Jefferson City, MO 65101-9564

In [112]: df[['City','State','ZIP']] = df['Zip State City'].str.extract(r'([^,\d]+)?[,]*\s*([A-Z]{2})?\s*([\d\-]{4,11})?', expand=True)

In [113]: df
Out[113]:
                               Company                 Zip State City            City State         ZIP
1                                *CBRE            San Diego, CA 92101       San Diego    CA       92101
4                          1908 Brands              Boulder, CO 80301         Boulder    CO       80301
7   1st Infantry Division Headquarters                 Fort Riley, KS      Fort Riley    KS         NaN
10       21st Century Healthcare, Inc.                    Tempe 85282          Tempe    NaN       85282
15                                 AAA  Jefferson City, MO 65101-9564  Jefferson City    MO  65101-9564

从docs开始：

Series.str.extract(pat, flags=0, expand=None)

For each subject string in the Series, extract groups from the first
match of regular expression pat.

New in version 0.13.0.

Parameters:

pat : string

Regular expression pattern with capturing groups

flags : int, default 0 (no flags)

re module flags, e.g.
re.IGnorECASE .. versionadded:: 0.18.0

expand : bool, default False

If True, return DataFrame.

If False, return Series/Index/DataFrame.

Returns: DataFrame with one row for each subject string, and one
column for each group. Any capture group names in regular expression
pat will be used for column names; otherwise capture group numbers
will be used. The dtype of each result column is always object, even
when no match is found. If expand=True and pat has only one capture
group, then return a Series (if subject is a Series) or Index (if
subject is an Index).