在pandas DataFrame中快速应用字符串操作 [英] Quickly applying string operations in a pandas DataFrame

问题描述

假设我有一个DataFrame，其中有10万行和一列name.我想尽可能有效地将这个名字分成姓和名.我目前的方法是

Suppose I have a DataFrame with 100k rows and a column name. I would like to split this name into first and last name as efficiently as possibly. My current method is,

def splitName(name):
  return pandas.Series(name.split()[0:2])

df[['first', 'last']] = df.apply(lambda x: splitName(x['name']), axis=1)

不幸的是，DataFrame.apply确实非常慢.我有什么办法可以使此字符串操作几乎与numpy操作一样快?

Unfortunately, DataFrame.apply is really, really slow. Is there anything I can do to make this string operation nearly as fast as a numpy operation?

谢谢！

推荐答案

尝试(要求熊猫> = 0.8.1):

Try (requires pandas >= 0.8.1):

splits = x['name'].split()
df['first'] = splits.str[0]
df['last'] = splits.str[1]

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