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split（）正序分割列；rsplit（）逆序分割列
Series.str.split(pat=None, n=-1, expand=False)
參數(shù):
pat : 字符串,默認(rèn)使用空白分割.
n : 整型,默認(rèn)為-1,既使用所有的分割點(diǎn)分割
expand : 布爾值,默認(rèn)為False.如果為真返回數(shù)據(jù)框(DataFrame)或復(fù)雜索引(MultiIndex);如果為True,返回序列(Series)或者索引(Index).
return_type : 棄用,使用spand參數(shù)代替
返回值:
split : 參考expand參數(shù)

例子：
將一下列表按第一個空格分割成兩個列表，列表的名稱分別是“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")#讀取工作表df["Property"],df["Description"]=df["Property Description"].str.split(" ",n=1).str#按第一個空格分割df.drop("Property Description",axis=1,inplace=True)#刪除原有的列df.to_csv("C:/Users/Administrator/Desktop/New Microsoft Excel 工作表.csv",index=False)#保存為csv，并刪除索引

結(jié)果如下圖所示：

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

上述就是小編為大家分享的 Pandas中split()方法如何使用了，如果剛好有類似的疑惑，不妨參照上述分析進(jìn)行理解。如果想知道更多相關(guān)知識，歡迎關(guān)注創(chuàng)新互聯(lián)行業(yè)資訊頻道。

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