Python 如何在pandas中进行关键字映射
我有关键字Python 如何在pandas中进行关键字映射,python,pandas,word2vec,Python,Pandas,Word2vec,我有关键字 India Japan United States Germany China 下面是示例数据帧 id Address 1 Chome-2-8 Shibakoen, Minato, Tokyo 105-0011, Japan 2 Arcisstraße 21, 80333 München, Germany 3 Liberty Street, Manhattan, New York, United States 4 30 Shuangqing
India
Japan
United States
Germany
China
下面是示例数据帧
id Address
1 Chome-2-8 Shibakoen, Minato, Tokyo 105-0011, Japan
2 Arcisstraße 21, 80333 München, Germany
3 Liberty Street, Manhattan, New York, United States
4 30 Shuangqing Rd, Haidian Qu, Beijing Shi, China
5 Vaishnavi Summit,80feet Road,3rd Block,Bangalore, Karnataka, India
我的目标是制造
id Address India Japan United States Germany China
1 Chome-2-8 Shibakoen, Minato, Tokyo 105-0011, Japan 0 1 0 0 0
2 Arcisstraße 21, 80333 München, Germany 0 0 0 1 0
3 Liberty Street, Manhattan, New York, USA 0 0 1 0 0
4 30 Shuangqing Rd, Haidian Qu, Beijing Shi, China 0 0 0 0 1
5 Vaishnavi Summit,80feet Road,Bangalore, Karnataka, India 1 0 0 0 0
基本思想是创建关键字检测器,我想使用str.contain
和word2vec
,但我无法得到逻辑
In [58]: df = df.join(df.Address.str.extract(r'.*,(.*)', expand=False).str.get_dummies())
In [59]: df
Out[59]:
id Address China Germany India Japan United States
0 1 Chome-2-8 Shibakoen, Minato, Tokyo 105-0011, J... 0 0 0 1 0
1 2 Arcisstra?e 21, 80333 Munchen, Germany 0 1 0 0 0
2 3 Liberty Street, Manhattan, New York, United St... 0 0 0 0 1
3 4 30 Shuangqing Rd, Haidian Qu, Beijing Shi, China 1 0 0 0 0
4 5 Vaishnavi Summit,80feet Road,3rd Block,Bangalo... 0 0 1 0 0
注意:如果国家/地区不在
地址
列的最后一个位置,或者如果国家/地区名称包含,
请使用pd.get\u dummies()
:
此外,最直接的方法是将国家列在一个列表中,并使用for循环,比如
countries = ['India','Japan','United States','Germany','China']
for c in countries:
df[c] = df.Address.str.contains(c) * 1
但是如果你有很多数据和国家,速度可能会很慢。我正在打电话。我从头顶回答。你能确认我的答案有效吗?这是str.find的ufunc。我可以使用跨地址和关键字的广播。如果找到关键字,则返回位置。否则返回-1。因此>=0谢谢你。我会在几个小时后把它修好,等我回到电脑前。@piRSquared,对不起,我瞎了。我没有从numpy.core.defchararray import find中看到
-现在它按预期工作:)不确定您是否看到了这个。我特别为它感到骄傲(-:这有一个拼写错误,不能运行。
from numpy.core.defchararray import find
kw = 'India|Japan|United States|Germany|China'.split('|')
a = df.Address.values.astype(str)[:, None]
df.join(
pd.DataFrame(
find(a, kw) >= 0,
df.index, kw,
dtype=int
)
)
id Address India Japan United States Germany China
0 1 Chome-2-8 Shibakoen, Minat... 0 1 0 0 0
1 2 Arcisstraße 21, 80333 Münc... 0 0 0 1 0
2 3 Liberty Street, Manhattan,... 0 0 1 0 0
3 4 30 Shuangqing Rd, Haidian ... 0 0 0 0 1
4 5 Vaishnavi Summit,80feet Ro... 1 0 0 0 0
from numpy.core.defchararray import find
kw = 'India|Japan|United States|Germany|China'.split('|')
a = df.Address.values.astype(str)[:, None]
df.join(
pd.DataFrame(
find(a, kw) >= 0,
df.index, kw,
dtype=int
)
)
id Address India Japan United States Germany China
0 1 Chome-2-8 Shibakoen, Minat... 0 1 0 0 0
1 2 Arcisstraße 21, 80333 Münc... 0 0 0 1 0
2 3 Liberty Street, Manhattan,... 0 0 1 0 0
3 4 30 Shuangqing Rd, Haidian ... 0 0 0 0 1
4 5 Vaishnavi Summit,80feet Ro... 1 0 0 0 0