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。
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，欢迎关注亿速云行业资讯频道。
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