这期内容当中小编将会给大家带来有关 Pandas中split()方法如何使用，文章内容丰富且以专业的角度为大家分析和叙述，阅读完这篇文章希望大家可以有所收获。

创新互联于2013年成立，先为开封等服务建站，开封等地企业，进行企业商务咨询服务。为开封企业网站制作PC+手机+微官网三网同步一站式服务解决您的所有建站问题。

split（）正序分割列；rsplit（）逆序分割列
Series.str.split(pat=None, n=-1, expand=False)
参数:
pat : 字符串,默认使用空白分割.
n : 整型,默认为-1,既使用所有的分割点分割
expand : 布尔值,默认为False.如果为真返回数据框(DataFrame)或复杂索引(MultiIndex);如果为True,返回序列(Series)或者索引(Index).
return_type : 弃用,使用spand参数代替
返回值:
split : 参考expand参数

例子：
将一下列表按第一个空格分割成两个列表，列表的名称分别是“Property”和“Description”

Property Description

year The year of the datetime

month The month of the datetime

day The days of the datetime

hour The hour of the datetime

minute The minutes of the datetime

second The seconds of the datetime

microsecond The microseconds of the datetime

nanosecond The nanoseconds of the datetime

date Returns datetime.date (does not contain timezone information)

time Returns datetime.time (does not contain timezone information)

dayofyear The ordinal day of year

weekofyear The week ordinal of the year

week The week ordinal of the year

dayofweek The numer of the day of the week with Monday=0, Sunday=6

weekday The number of the day of the week with Monday=0, Sunday=6

weekday_name The name of the day in a week (ex: Friday)

quarter Quarter of the date: Jan=Mar = 1, Apr-Jun = 2, etc.

days_in_month The number of days in the month of the datetime

is_month_start Logical indicating if first day of month (defined by frequency)

is_month_end Logical indicating if last day of month (defined by frequency)

is_quarter_start Logical indicating if first day of quarter (defined by frequency)

is_quarter_end Logical indicating if last day of quarter (defined by frequency)

is_year_start Logical indicating if first day of year (defined by frequency)

is_year_end Logical indicating if last day of year (defined by frequency)

is_leap_year Logical indicating if the date belongs to a leap year

import pandas as pd
df=pd.read_excel("C:/Users/Administrator/Desktop/New Microsoft Excel 工作表.xlsx")#读取工作表df["Property"],df["Description"]=df["Property Description"].str.split(" ",n=1).str#按第一个空格分割df.drop("Property Description",axis=1,inplace=True)#删除原有的列df.to_csv("C:/Users/Administrator/Desktop/New Microsoft Excel 工作表.csv",index=False)#保存为csv，并删除索引

结果如下图所示：

Property	Description
year	The year of the datetime
month	The month of the datetime
day	The days of the datetime
hour	The hour of the datetime
minute	The minutes of the datetime
second	The seconds of the datetime
microsecond	The microseconds of the datetime
nanosecond	The nanoseconds of the datetime
date	Returns datetime.date (does not contain timezone information)
time	Returns datetime.time (does not contain timezone information)
dayofyear	The ordinal day of year
weekofyear	The week ordinal of the year
week	The week ordinal of the year
dayofweek	The numer of the day of the week with Monday=0, Sunday=6
weekday	The number of the day of the week with Monday=0, Sunday=6
weekday_name	The name of the day in a week (ex: Friday)
quarter	Quarter of the date: Jan=Mar = 1, Apr-Jun = 2, etc.
days_in_month	The number of days in the month of the datetime
is_month_start	Logical indicating if first day of month (defined by frequency)
is_month_end	Logical indicating if last day of month (defined by frequency)
is_quarter_start	Logical indicating if first day of quarter (defined by frequency)
is_quarter_end	Logical indicating if last day of quarter (defined by frequency)
is_year_start	Logical indicating if first day of year (defined by frequency)
is_year_end	Logical indicating if last day of year (defined by frequency)
is_leap_year	Logical indicating if the date belongs to a leap year

上述就是小编为大家分享的 Pandas中split()方法如何使用了，如果刚好有类似的疑惑，不妨参照上述分析进行理解。如果想知道更多相关知识，欢迎关注创新互联行业资讯频道。

网站标题：Pandas中split()方法如何使用
地址分享：http://chengdu.cdxwcx.cn/article/jeidoc.html