Pandas text data method split () Rsplit ()

Split () positive sequence split column; Rsplit () split column in reverse order
Series.str.split (Pat=none, N=-1, Expand=false)
Parameters:
Pat: string, default use of white space split.
N: Integer, default to-1, use all split points to split
Expand: Boolean value, default to False. Returns a data box (Dataframe) or a complex index (MULTIINDEX) if true, or a sequence (Series) or index (indexed) if it is.
Return_type: Discard, use Spand parameter instead
return value:
Split: Reference Expand Parameters

Example:
Divide the list by the first space into two lists, which are named "Property" and "Description".

Property Description

Year of the DateTime

Month the month of the DateTime

Day 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 the year

WeekOfYear the week ordinal of the year

Week the week ordinal of the year

DayOfWeek the numer of the week with Monday=0, sunday=6

Weekday 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 a month (defined by frequency)

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

Is_quarter_start Logical indicating if a quarter (defined by frequency)

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

Is_year_start Logical indicating if (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 worksheet. xlsx") #读取工作表
DF [Property],df[' Description ']=df[' property Description '].str.split ("", n=1). str# divide by first space
Df.drop ("Property Description ", axis=1,inplace=true) #删除原有的列
df.to_csv (" C:/users/administrator/desktop/new Microsoft Excel Worksheet. csv ", Index=false) #保存为csv, and delete the index

The results are shown in the following illustration:

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 the year
WeekOfYear	The week ordinal of the year
Week	The week ordinal of the year
DayOfWeek	The numer 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 a month (defined by frequency)
Is_month_end	Logical indicating if last day of month (defined by frequency)
Is_quarter_start	Logical indicating if a quarter (defined by frequency)
Is_quarter_end	Logical indicating if last day of quarter (defined by frequency)
Is_year_start	Logical indicating if (defined by frequency)
Is_year_end	Logical indicating if (defined by frequency)
is_leap_year	Logical indicating if the date belongs to a leap year