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這期內容當中小編將會給大家帶來有關 Pandas中split()方法如何使用，文章內容豐富且以專業的角度為大家分析和敘述，閱讀完這篇文章希望大家可以有所收獲。
split（）正序分割列；rsplit（）逆序分割列
Series.str.split(pat=None, n=-1, expand=False)
參數:
pat : 字符串,默認使用空白分割.
n : 整型,默認為-1,既使用所有的分割點分割
expand : 布爾值,默認為False.如果為真返回數據框(DataFrame)或復雜索引(MultiIndex);如果為True,返回序列(Series)或者索引(Index).
return_type : 棄用,使用spand參數代替
返回值:
split : 參考expand參數
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行理解。如果想知道更多相關知識，歡迎關注億速云行業資訊頻道。
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