如何从包含文本的熊猫数据框中的列中提取年份（或日期时间）
问题描述：
假设我有一个熊猫数据框：
Suppose I have a pandas dataframe:
Id Book
1 Harry Potter (1997)
2 Of Mice and Men (1937)
3 Babe Ruth Story, The (1948) Drama 948) Babe Ruth Story
如何从列中提取年份？
输出应为：
Id Book Title Year
1 Harry Potter 1997
2 Of Mice and Men 1937
3 Babe Ruth Story, The 1948
到目前为止，我已经尝试过：
So far I have tried:
movies['year'] = movies['title'].str.extract('([0-9(0-9)]+)', expand=False).str.strip()
和
books['year'] = books['title'].str[-5:-1]
我搞砸了一些其他事情，还没有开始工作。有建议吗？
I've messed around with some other things and haven't gotten it to work yet. Any suggestions?
答
简单的正则表达式如何：
How about a simple Regex:
text = 'Harry Potter (1997)'
re.findall('\((\d{4})\)', text)
# ['1997'] Note that this is a list of "all" the occurrences.
使用Dataframe可以像这样完成：
With a Dataframe, it can be done like this:
text = 'Harry Potter (1997)'
df = pd.DataFrame({'Book': text}, index=[1])
pattern = '\((\d{4})\)'
df['year'] = df.Book.str.extract(pattern, expand=False) #False returns a series
df
# Book year
# 1 Harry Potter (1997) 1997
最后，如果您确实想分隔标题和数据（将Philip的数据框重建为另一个答案）：
Finally, if you actually want to separate the title and the data (taking the dataframe reconstruction from Philip in another answer):
df = pd.DataFrame(columns=['Book'], data=[['Harry Potter (1997)'],['Of Mice and Men (1937)'],['Babe Ruth Story, The (1948) Drama 948) Babe Ruth Story']])
sep = df['Book'].str.extract('(.*)\((\d{4})\)', expand=False)
sep # A new df, separated into title and year
# 0 1
# 0 Harry Potter 1997
# 1 Of Mice and Men 1937
# 2 Babe Ruth Story, The 1948