pandas.tseries.offsets.CustomBusinessHour   classpandas.tseries.offsets.CustomBusinessHour

DateOffset subclass representing possibly n custom business days.  Parameters

n:int, default 1

The number of months represented.
normalize:bool, default False

Normalize start/end dates to midnight before generating date range.
weekmask:str, Default ‘Mon Tue Wed Thu Fri’

Weekmask of valid business days, passed to numpy.busdaycalendar.
start:str, default “09:00”

Start time of your custom business hour in 24h format.
end:str, default: “17:00”

End time of your custom business hour in 24h format.     Attributes
base Returns a copy of the calling offset object with n=1 and all other attributes equal.
next_bday Used for moving to next business day.
offset Alias for self._offset.
calendar
end
freqstr
holidays
kwds
n
name
nanos
normalize
rule_code
start
weekmask     Methods
__call__(*args, **kwargs) Call self as a function.
rollback(other) Roll provided date backward to next offset only if not on offset.
rollforward(other) Roll provided date forward to next offset only if not on offset.
apply
apply_index
copy
isAnchored
is_anchored
is_month_end
is_month_start
is_on_offset
is_quarter_end
is_quarter_start
is_year_end
is_year_start
onOffset


pandas.tseries.offsets.CustomBusinessHour.__call__   CustomBusinessHour.__call__(*args, **kwargs)

Call self as a function.


pandas.tseries.offsets.CustomBusinessHour.apply   CustomBusinessHour.apply()


pandas.tseries.offsets.CustomBusinessHour.apply_index   CustomBusinessHour.apply_index(other)


pandas.tseries.offsets.CustomBusinessHour.base   CustomBusinessHour.base

Returns a copy of the calling offset object with n=1 and all other attributes equal.


pandas.tseries.offsets.CustomBusinessHour.calendar   CustomBusinessHour.calendar


pandas.tseries.offsets.CustomBusinessHour.copy   CustomBusinessHour.copy()


pandas.tseries.offsets.CustomBusinessHour.end   CustomBusinessHour.end


pandas.tseries.offsets.CustomBusinessHour.freqstr   CustomBusinessHour.freqstr


pandas.tseries.offsets.CustomBusinessHour.holidays   CustomBusinessHour.holidays


pandas.tseries.offsets.CustomBusinessHour.is_anchored   CustomBusinessHour.is_anchored()


pandas.tseries.offsets.CustomBusinessHour.is_month_end   CustomBusinessHour.is_month_end()


pandas.tseries.offsets.CustomBusinessHour.is_month_start   CustomBusinessHour.is_month_start()


pandas.tseries.offsets.CustomBusinessHour.is_on_offset   CustomBusinessHour.is_on_offset()


pandas.tseries.offsets.CustomBusinessHour.is_quarter_end   CustomBusinessHour.is_quarter_end()


pandas.tseries.offsets.CustomBusinessHour.is_quarter_start   CustomBusinessHour.is_quarter_start()


pandas.tseries.offsets.CustomBusinessHour.is_year_end   CustomBusinessHour.is_year_end()


pandas.tseries.offsets.CustomBusinessHour.is_year_start   CustomBusinessHour.is_year_start()


pandas.tseries.offsets.CustomBusinessHour.isAnchored   CustomBusinessHour.isAnchored()


pandas.tseries.offsets.CustomBusinessHour.kwds   CustomBusinessHour.kwds


pandas.tseries.offsets.CustomBusinessHour.n   CustomBusinessHour.n


pandas.tseries.offsets.CustomBusinessHour.name   CustomBusinessHour.name


pandas.tseries.offsets.CustomBusinessHour.nanos   CustomBusinessHour.nanos


pandas.tseries.offsets.CustomBusinessHour.next_bday   CustomBusinessHour.next_bday

Used for moving to next business day.


pandas.tseries.offsets.CustomBusinessHour.normalize   CustomBusinessHour.normalize


pandas.tseries.offsets.CustomBusinessHour.offset   CustomBusinessHour.offset

Alias for self._offset.


pandas.tseries.offsets.CustomBusinessHour.onOffset   CustomBusinessHour.onOffset()


pandas.tseries.offsets.CustomBusinessHour.rollback   CustomBusinessHour.rollback(other)

Roll provided date backward to next offset only if not on offset.


pandas.tseries.offsets.CustomBusinessHour.rollforward   CustomBusinessHour.rollforward(other)

Roll provided date forward to next offset only if not on offset.


pandas.tseries.offsets.CustomBusinessHour.rule_code   CustomBusinessHour.rule_code


pandas.tseries.offsets.CustomBusinessHour.start   CustomBusinessHour.start


pandas.tseries.offsets.CustomBusinessHour.weekmask   CustomBusinessHour.weekmask


pandas.tseries.offsets.CustomBusinessMonthBegin   classpandas.tseries.offsets.CustomBusinessMonthBegin

Attributes
base Returns a copy of the calling offset object with n=1 and all other attributes equal.
cbday_roll Define default roll function to be called in apply method.
month_roll Define default roll function to be called in apply method.
offset Alias for self._offset.
calendar
freqstr
holidays
kwds
m_offset
n
name
nanos
normalize
rule_code
weekmask     Methods
__call__(*args, **kwargs) Call self as a function.
rollback Roll provided date backward to next offset only if not on offset.
rollforward Roll provided date forward to next offset only if not on offset.
apply
apply_index
copy
isAnchored
is_anchored
is_month_end
is_month_start
is_on_offset
is_quarter_end
is_quarter_start
is_year_end
is_year_start
onOffset


pandas.tseries.offsets.CustomBusinessMonthBegin.__call__   CustomBusinessMonthBegin.__call__(*args, **kwargs)

Call self as a function.


pandas.tseries.offsets.CustomBusinessMonthBegin.apply   CustomBusinessMonthBegin.apply()


pandas.tseries.offsets.CustomBusinessMonthBegin.apply_index   CustomBusinessMonthBegin.apply_index(other)


pandas.tseries.offsets.CustomBusinessMonthBegin.base   CustomBusinessMonthBegin.base

Returns a copy of the calling offset object with n=1 and all other attributes equal.


pandas.tseries.offsets.CustomBusinessMonthBegin.calendar   CustomBusinessMonthBegin.calendar


pandas.tseries.offsets.CustomBusinessMonthBegin.cbday_roll   CustomBusinessMonthBegin.cbday_roll

Define default roll function to be called in apply method.


pandas.tseries.offsets.CustomBusinessMonthBegin.copy   CustomBusinessMonthBegin.copy()


pandas.tseries.offsets.CustomBusinessMonthBegin.freqstr   CustomBusinessMonthBegin.freqstr


pandas.tseries.offsets.CustomBusinessMonthBegin.holidays   CustomBusinessMonthBegin.holidays


pandas.tseries.offsets.CustomBusinessMonthBegin.is_anchored   CustomBusinessMonthBegin.is_anchored()


pandas.tseries.offsets.CustomBusinessMonthBegin.is_month_end   CustomBusinessMonthBegin.is_month_end()


pandas.tseries.offsets.CustomBusinessMonthBegin.is_month_start   CustomBusinessMonthBegin.is_month_start()


pandas.tseries.offsets.CustomBusinessMonthBegin.is_on_offset   CustomBusinessMonthBegin.is_on_offset()


pandas.tseries.offsets.CustomBusinessMonthBegin.is_quarter_end   CustomBusinessMonthBegin.is_quarter_end()


pandas.tseries.offsets.CustomBusinessMonthBegin.is_quarter_start   CustomBusinessMonthBegin.is_quarter_start()


pandas.tseries.offsets.CustomBusinessMonthBegin.is_year_end   CustomBusinessMonthBegin.is_year_end()


pandas.tseries.offsets.CustomBusinessMonthBegin.is_year_start   CustomBusinessMonthBegin.is_year_start()


pandas.tseries.offsets.CustomBusinessMonthBegin.isAnchored   CustomBusinessMonthBegin.isAnchored()


pandas.tseries.offsets.CustomBusinessMonthBegin.kwds   CustomBusinessMonthBegin.kwds


pandas.tseries.offsets.CustomBusinessMonthBegin.m_offset   CustomBusinessMonthBegin.m_offset


pandas.tseries.offsets.CustomBusinessMonthBegin.month_roll   CustomBusinessMonthBegin.month_roll

Define default roll function to be called in apply method.


pandas.tseries.offsets.CustomBusinessMonthBegin.n   CustomBusinessMonthBegin.n


pandas.tseries.offsets.CustomBusinessMonthBegin.name   CustomBusinessMonthBegin.name


pandas.tseries.offsets.CustomBusinessMonthBegin.nanos   CustomBusinessMonthBegin.nanos


pandas.tseries.offsets.CustomBusinessMonthBegin.normalize   CustomBusinessMonthBegin.normalize


pandas.tseries.offsets.CustomBusinessMonthBegin.offset   CustomBusinessMonthBegin.offset

Alias for self._offset.


pandas.tseries.offsets.CustomBusinessMonthBegin.onOffset   CustomBusinessMonthBegin.onOffset()


pandas.tseries.offsets.CustomBusinessMonthBegin.rollback   CustomBusinessMonthBegin.rollback()

Roll provided date backward to next offset only if not on offset.  Returns
 TimeStamp

Rolled timestamp if not on offset, otherwise unchanged timestamp.


pandas.tseries.offsets.CustomBusinessMonthBegin.rollforward   CustomBusinessMonthBegin.rollforward()

Roll provided date forward to next offset only if not on offset.  Returns
 TimeStamp

Rolled timestamp if not on offset, otherwise unchanged timestamp.


pandas.tseries.offsets.CustomBusinessMonthBegin.rule_code   CustomBusinessMonthBegin.rule_code


pandas.tseries.offsets.CustomBusinessMonthBegin.weekmask   CustomBusinessMonthBegin.weekmask


pandas.tseries.offsets.CustomBusinessMonthEnd   classpandas.tseries.offsets.CustomBusinessMonthEnd

Attributes
base Returns a copy of the calling offset object with n=1 and all other attributes equal.
cbday_roll Define default roll function to be called in apply method.
month_roll Define default roll function to be called in apply method.
offset Alias for self._offset.
calendar
freqstr
holidays
kwds
m_offset
n
name
nanos
normalize
rule_code
weekmask     Methods
__call__(*args, **kwargs) Call self as a function.
rollback Roll provided date backward to next offset only if not on offset.
rollforward Roll provided date forward to next offset only if not on offset.
apply
apply_index
copy
isAnchored
is_anchored
is_month_end
is_month_start
is_on_offset
is_quarter_end
is_quarter_start
is_year_end
is_year_start
onOffset


pandas.tseries.offsets.CustomBusinessMonthEnd.__call__   CustomBusinessMonthEnd.__call__(*args, **kwargs)

Call self as a function.


pandas.tseries.offsets.CustomBusinessMonthEnd.apply   CustomBusinessMonthEnd.apply()


pandas.tseries.offsets.CustomBusinessMonthEnd.apply_index   CustomBusinessMonthEnd.apply_index(other)


pandas.tseries.offsets.CustomBusinessMonthEnd.base   CustomBusinessMonthEnd.base

Returns a copy of the calling offset object with n=1 and all other attributes equal.


pandas.tseries.offsets.CustomBusinessMonthEnd.calendar   CustomBusinessMonthEnd.calendar


pandas.tseries.offsets.CustomBusinessMonthEnd.cbday_roll   CustomBusinessMonthEnd.cbday_roll

Define default roll function to be called in apply method.


pandas.tseries.offsets.CustomBusinessMonthEnd.copy   CustomBusinessMonthEnd.copy()


pandas.tseries.offsets.CustomBusinessMonthEnd.freqstr   CustomBusinessMonthEnd.freqstr


pandas.tseries.offsets.CustomBusinessMonthEnd.holidays   CustomBusinessMonthEnd.holidays


pandas.tseries.offsets.CustomBusinessMonthEnd.is_anchored   CustomBusinessMonthEnd.is_anchored()


pandas.tseries.offsets.CustomBusinessMonthEnd.is_month_end   CustomBusinessMonthEnd.is_month_end()


pandas.tseries.offsets.CustomBusinessMonthEnd.is_month_start   CustomBusinessMonthEnd.is_month_start()


pandas.tseries.offsets.CustomBusinessMonthEnd.is_on_offset   CustomBusinessMonthEnd.is_on_offset()


pandas.tseries.offsets.CustomBusinessMonthEnd.is_quarter_end   CustomBusinessMonthEnd.is_quarter_end()


pandas.tseries.offsets.CustomBusinessMonthEnd.is_quarter_start   CustomBusinessMonthEnd.is_quarter_start()


pandas.tseries.offsets.CustomBusinessMonthEnd.is_year_end   CustomBusinessMonthEnd.is_year_end()


pandas.tseries.offsets.CustomBusinessMonthEnd.is_year_start   CustomBusinessMonthEnd.is_year_start()


pandas.tseries.offsets.CustomBusinessMonthEnd.isAnchored   CustomBusinessMonthEnd.isAnchored()


pandas.tseries.offsets.CustomBusinessMonthEnd.kwds   CustomBusinessMonthEnd.kwds


pandas.tseries.offsets.CustomBusinessMonthEnd.m_offset   CustomBusinessMonthEnd.m_offset


pandas.tseries.offsets.CustomBusinessMonthEnd.month_roll   CustomBusinessMonthEnd.month_roll

Define default roll function to be called in apply method.


pandas.tseries.offsets.CustomBusinessMonthEnd.n   CustomBusinessMonthEnd.n


pandas.tseries.offsets.CustomBusinessMonthEnd.name   CustomBusinessMonthEnd.name


pandas.tseries.offsets.CustomBusinessMonthEnd.nanos   CustomBusinessMonthEnd.nanos


pandas.tseries.offsets.CustomBusinessMonthEnd.normalize   CustomBusinessMonthEnd.normalize


pandas.tseries.offsets.CustomBusinessMonthEnd.offset   CustomBusinessMonthEnd.offset

Alias for self._offset.


pandas.tseries.offsets.CustomBusinessMonthEnd.onOffset   CustomBusinessMonthEnd.onOffset()


pandas.tseries.offsets.CustomBusinessMonthEnd.rollback   CustomBusinessMonthEnd.rollback()

Roll provided date backward to next offset only if not on offset.  Returns
 TimeStamp

Rolled timestamp if not on offset, otherwise unchanged timestamp.


pandas.tseries.offsets.CustomBusinessMonthEnd.rollforward   CustomBusinessMonthEnd.rollforward()

Roll provided date forward to next offset only if not on offset.  Returns
 TimeStamp

Rolled timestamp if not on offset, otherwise unchanged timestamp.


pandas.tseries.offsets.CustomBusinessMonthEnd.rule_code   CustomBusinessMonthEnd.rule_code


pandas.tseries.offsets.CustomBusinessMonthEnd.weekmask   CustomBusinessMonthEnd.weekmask


pandas.tseries.offsets.DateOffset   classpandas.tseries.offsets.DateOffset

Standard kind of date increment used for a date range. Works exactly like the keyword argument form of relativedelta. Note that the positional argument form of relativedelata is not supported. Use of the keyword n is discouraged– you would be better off specifying n in the keywords you use, but regardless it is there for you. n is needed for DateOffset subclasses. DateOffset works as follows. Each offset specify a set of dates that conform to the DateOffset. For example, Bday defines this set to be the set of dates that are weekdays (M-F). To test if a date is in the set of a DateOffset dateOffset we can use the is_on_offset method: dateOffset.is_on_offset(date). If a date is not on a valid date, the rollback and rollforward methods can be used to roll the date to the nearest valid date before/after the date. DateOffsets can be created to move dates forward a given number of valid dates. For example, Bday(2) can be added to a date to move it two business days forward. If the date does not start on a valid date, first it is moved to a valid date. Thus pseudo code is:  def __add__(date):

date = rollback(date) # does nothing if date is valid return date + <n number of periods>   When a date offset is created for a negative number of periods, the date is first rolled forward. The pseudo code is:  def __add__(date):

date = rollforward(date) # does nothing is date is valid return date + <n number of periods>   Zero presents a problem. Should it roll forward or back? We arbitrarily have it rollforward: date + BDay(0) == BDay.rollforward(date) Since 0 is a bit weird, we suggest avoiding its use.  Parameters

n:int, default 1

The number of time periods the offset represents.
normalize:bool, default False

Whether to round the result of a DateOffset addition down to the previous midnight.  **kwds

Temporal parameter that add to or replace the offset value. Parameters that add to the offset (like Timedelta):  years months weeks days hours minutes seconds microseconds nanoseconds  Parameters that replace the offset value:  year month day weekday hour minute second microsecond nanosecond.       See also  dateutil.relativedelta.relativedelta

The relativedelta type is designed to be applied to an existing datetime an can replace specific components of that datetime, or represents an interval of time.    Examples
>>> from pandas.tseries.offsets import DateOffset
>>> ts = pd.Timestamp('2017-01-01 09:10:11')
>>> ts + DateOffset(months=3)
Timestamp('2017-04-01 09:10:11')

>>> ts = pd.Timestamp('2017-01-01 09:10:11')
>>> ts + DateOffset(months=2)
Timestamp('2017-03-01 09:10:11')
  Attributes
base Returns a copy of the calling offset object with n=1 and all other attributes equal.
freqstr
kwds
n
name
nanos
normalize
rule_code     Methods
__call__(*args, **kwargs) Call self as a function.
rollback Roll provided date backward to next offset only if not on offset.
rollforward Roll provided date forward to next offset only if not on offset.
apply
apply_index
copy
isAnchored
is_anchored
is_month_end
is_month_start
is_on_offset
is_quarter_end
is_quarter_start
is_year_end
is_year_start
onOffset


pandas.tseries.offsets.DateOffset.__call__   DateOffset.__call__(*args, **kwargs)

Call self as a function.


pandas.tseries.offsets.DateOffset.apply   DateOffset.apply()


pandas.tseries.offsets.DateOffset.apply_index   DateOffset.apply_index(other)


pandas.tseries.offsets.DateOffset.base   DateOffset.base

Returns a copy of the calling offset object with n=1 and all other attributes equal.


pandas.tseries.offsets.DateOffset.copy   DateOffset.copy()


pandas.tseries.offsets.DateOffset.freqstr   DateOffset.freqstr


pandas.tseries.offsets.DateOffset.is_anchored   DateOffset.is_anchored()


pandas.tseries.offsets.DateOffset.is_month_end   DateOffset.is_month_end()


pandas.tseries.offsets.DateOffset.is_month_start   DateOffset.is_month_start()


pandas.tseries.offsets.DateOffset.is_on_offset   DateOffset.is_on_offset()


pandas.tseries.offsets.DateOffset.is_quarter_end   DateOffset.is_quarter_end()


pandas.tseries.offsets.DateOffset.is_quarter_start   DateOffset.is_quarter_start()


pandas.tseries.offsets.DateOffset.is_year_end   DateOffset.is_year_end()


pandas.tseries.offsets.DateOffset.is_year_start   DateOffset.is_year_start()


pandas.tseries.offsets.DateOffset.isAnchored   DateOffset.isAnchored()


pandas.tseries.offsets.DateOffset.kwds   DateOffset.kwds


pandas.tseries.offsets.DateOffset.n   DateOffset.n


pandas.tseries.offsets.DateOffset.name   DateOffset.name


pandas.tseries.offsets.DateOffset.nanos   DateOffset.nanos


pandas.tseries.offsets.DateOffset.normalize   DateOffset.normalize


pandas.tseries.offsets.DateOffset.onOffset   DateOffset.onOffset()


pandas.tseries.offsets.DateOffset.rollback   DateOffset.rollback()

Roll provided date backward to next offset only if not on offset.  Returns
 TimeStamp

Rolled timestamp if not on offset, otherwise unchanged timestamp.


pandas.tseries.offsets.DateOffset.rollforward   DateOffset.rollforward()

Roll provided date forward to next offset only if not on offset.  Returns
 TimeStamp

Rolled timestamp if not on offset, otherwise unchanged timestamp.


pandas.tseries.offsets.DateOffset.rule_code   DateOffset.rule_code


pandas.tseries.offsets.Day   classpandas.tseries.offsets.Day

Attributes
base Returns a copy of the calling offset object with n=1 and all other attributes equal.
delta
freqstr
kwds
n
name
nanos
normalize
rule_code     Methods
__call__(*args, **kwargs) Call self as a function.
rollback Roll provided date backward to next offset only if not on offset.
rollforward Roll provided date forward to next offset only if not on offset.
apply
apply_index
copy
isAnchored
is_anchored
is_month_end
is_month_start
is_on_offset
is_quarter_end
is_quarter_start
is_year_end
is_year_start
onOffset


pandas.tseries.offsets.Day.__call__   Day.__call__(*args, **kwargs)

Call self as a function.


pandas.tseries.offsets.Day.apply   Day.apply()


pandas.tseries.offsets.Day.apply_index   Day.apply_index(other)


pandas.tseries.offsets.Day.base   Day.base

Returns a copy of the calling offset object with n=1 and all other attributes equal.


pandas.tseries.offsets.Day.copy   Day.copy()


pandas.tseries.offsets.Day.delta   Day.delta


pandas.tseries.offsets.Day.freqstr   Day.freqstr


pandas.tseries.offsets.Day.is_anchored   Day.is_anchored()


pandas.tseries.offsets.Day.is_month_end   Day.is_month_end()


pandas.tseries.offsets.Day.is_month_start   Day.is_month_start()


pandas.tseries.offsets.Day.is_on_offset   Day.is_on_offset()


pandas.tseries.offsets.Day.is_quarter_end   Day.is_quarter_end()


pandas.tseries.offsets.Day.is_quarter_start   Day.is_quarter_start()


pandas.tseries.offsets.Day.is_year_end   Day.is_year_end()


pandas.tseries.offsets.Day.is_year_start   Day.is_year_start()


pandas.tseries.offsets.Day.isAnchored   Day.isAnchored()


pandas.tseries.offsets.Day.kwds   Day.kwds


pandas.tseries.offsets.Day.name   Day.name


pandas.tseries.offsets.Day.nanos   Day.nanos


pandas.tseries.offsets.Day.normalize   Day.normalize


pandas.tseries.offsets.Day.onOffset   Day.onOffset()


pandas.tseries.offsets.Day.rollback   Day.rollback()

Roll provided date backward to next offset only if not on offset.  Returns
 TimeStamp

Rolled timestamp if not on offset, otherwise unchanged timestamp.


pandas.tseries.offsets.Day.rollforward   Day.rollforward()

Roll provided date forward to next offset only if not on offset.  Returns
 TimeStamp

Rolled timestamp if not on offset, otherwise unchanged timestamp.


pandas.tseries.offsets.Day.rule_code   Day.rule_code


pandas.tseries.offsets.Easter   classpandas.tseries.offsets.Easter

DateOffset for the Easter holiday using logic defined in dateutil. Right now uses the revised method which is valid in years 1583-4099. Attributes
base Returns a copy of the calling offset object with n=1 and all other attributes equal.
freqstr
kwds
n
name
nanos
normalize
rule_code     Methods
__call__(*args, **kwargs) Call self as a function.
rollback Roll provided date backward to next offset only if not on offset.
rollforward Roll provided date forward to next offset only if not on offset.
apply
apply_index
copy
isAnchored
is_anchored
is_month_end
is_month_start
is_on_offset
is_quarter_end
is_quarter_start
is_year_end
is_year_start
onOffset


pandas.tseries.offsets.Easter.__call__   Easter.__call__(*args, **kwargs)

Call self as a function.


pandas.tseries.offsets.Easter.apply   Easter.apply()


pandas.tseries.offsets.Easter.apply_index   Easter.apply_index(other)


pandas.tseries.offsets.Easter.base   Easter.base

Returns a copy of the calling offset object with n=1 and all other attributes equal.


pandas.tseries.offsets.Easter.copy   Easter.copy()


pandas.tseries.offsets.Easter.freqstr   Easter.freqstr


pandas.tseries.offsets.Easter.is_anchored   Easter.is_anchored()


pandas.tseries.offsets.Easter.is_month_end   Easter.is_month_end()


pandas.tseries.offsets.Easter.is_month_start   Easter.is_month_start()


pandas.tseries.offsets.Easter.is_on_offset   Easter.is_on_offset()


pandas.tseries.offsets.Easter.is_quarter_end   Easter.is_quarter_end()


pandas.tseries.offsets.Easter.is_quarter_start   Easter.is_quarter_start()


pandas.tseries.offsets.Easter.is_year_end   Easter.is_year_end()


pandas.tseries.offsets.Easter.is_year_start   Easter.is_year_start()


pandas.tseries.offsets.Easter.isAnchored   Easter.isAnchored()


pandas.tseries.offsets.Easter.kwds   Easter.kwds


pandas.tseries.offsets.Easter.n   Easter.n


pandas.tseries.offsets.Easter.name   Easter.name


pandas.tseries.offsets.Easter.nanos   Easter.nanos


pandas.tseries.offsets.Easter.normalize   Easter.normalize


pandas.tseries.offsets.Easter.onOffset   Easter.onOffset()


pandas.tseries.offsets.Easter.rollback   Easter.rollback()

Roll provided date backward to next offset only if not on offset.  Returns
 TimeStamp

Rolled timestamp if not on offset, otherwise unchanged timestamp.


pandas.tseries.offsets.Easter.rollforward   Easter.rollforward()

Roll provided date forward to next offset only if not on offset.  Returns
 TimeStamp

Rolled timestamp if not on offset, otherwise unchanged timestamp.


pandas.tseries.offsets.Easter.rule_code   Easter.rule_code


pandas.tseries.offsets.FY5253   classpandas.tseries.offsets.FY5253

Describes 52-53 week fiscal year. This is also known as a 4-4-5 calendar. It is used by companies that desire that their fiscal year always end on the same day of the week. It is a method of managing accounting periods. It is a common calendar structure for some industries, such as retail, manufacturing and parking industry. For more information see: https://en.wikipedia.org/wiki/4-4-5_calendar The year may either:  end on the last X day of the Y month. end on the last X day closest to the last day of the Y month.  X is a specific day of the week. Y is a certain month of the year  Parameters

n:int

weekday:int {0, 1, …, 6}, default 0

A specific integer for the day of the week.  0 is Monday 1 is Tuesday 2 is Wednesday 3 is Thursday 4 is Friday 5 is Saturday 6 is Sunday.
startingMonth:int {1, 2, … 12}, default 1

The month in which the fiscal year ends.
variation:str, default “nearest”

Method of employing 4-4-5 calendar. There are two options:  “nearest” means year end is weekday closest to last day of month in year. “last” means year end is final weekday of the final month in fiscal year.      Attributes
base Returns a copy of the calling offset object with n=1 and all other attributes equal.
freqstr
kwds
n
name
nanos
normalize
rule_code
startingMonth
variation
weekday     Methods
__call__(*args, **kwargs) Call self as a function.
rollback Roll provided date backward to next offset only if not on offset.
rollforward Roll provided date forward to next offset only if not on offset.
apply
apply_index
copy
get_rule_code_suffix
get_year_end
isAnchored
is_anchored
is_month_end
is_month_start
is_on_offset
is_quarter_end
is_quarter_start
is_year_end
is_year_start
onOffset


pandas.tseries.offsets.FY5253.__call__   FY5253.__call__(*args, **kwargs)

Call self as a function.


pandas.tseries.offsets.FY5253.apply   FY5253.apply()


pandas.tseries.offsets.FY5253.apply_index   FY5253.apply_index(other)


pandas.tseries.offsets.FY5253.base   FY5253.base

Returns a copy of the calling offset object with n=1 and all other attributes equal.


pandas.tseries.offsets.FY5253.copy   FY5253.copy()


pandas.tseries.offsets.FY5253.freqstr   FY5253.freqstr


pandas.tseries.offsets.FY5253.get_rule_code_suffix   FY5253.get_rule_code_suffix()


pandas.tseries.offsets.FY5253.get_year_end   FY5253.get_year_end()


pandas.tseries.offsets.FY5253.is_anchored   FY5253.is_anchored()


pandas.tseries.offsets.FY5253.is_month_end   FY5253.is_month_end()


pandas.tseries.offsets.FY5253.is_month_start   FY5253.is_month_start()


pandas.tseries.offsets.FY5253.is_on_offset   FY5253.is_on_offset()


pandas.tseries.offsets.FY5253.is_quarter_end   FY5253.is_quarter_end()


pandas.tseries.offsets.FY5253.is_quarter_start   FY5253.is_quarter_start()


pandas.tseries.offsets.FY5253.is_year_end   FY5253.is_year_end()


pandas.tseries.offsets.FY5253.is_year_start   FY5253.is_year_start()


pandas.tseries.offsets.FY5253.isAnchored   FY5253.isAnchored()


pandas.tseries.offsets.FY5253.kwds   FY5253.kwds


pandas.tseries.offsets.FY5253.n   FY5253.n


pandas.tseries.offsets.FY5253.name   FY5253.name


pandas.tseries.offsets.FY5253.nanos   FY5253.nanos


pandas.tseries.offsets.FY5253.normalize   FY5253.normalize


pandas.tseries.offsets.FY5253.onOffset   FY5253.onOffset()


pandas.tseries.offsets.FY5253.rollback   FY5253.rollback()

Roll provided date backward to next offset only if not on offset.  Returns
 TimeStamp

Rolled timestamp if not on offset, otherwise unchanged timestamp.


pandas.tseries.offsets.FY5253.rollforward   FY5253.rollforward()

Roll provided date forward to next offset only if not on offset.  Returns
 TimeStamp

Rolled timestamp if not on offset, otherwise unchanged timestamp.


pandas.tseries.offsets.FY5253.rule_code   FY5253.rule_code


pandas.tseries.offsets.FY5253.startingMonth   FY5253.startingMonth


pandas.tseries.offsets.FY5253.variation   FY5253.variation


pandas.tseries.offsets.FY5253.weekday   FY5253.weekday


pandas.tseries.offsets.FY5253Quarter   classpandas.tseries.offsets.FY5253Quarter

DateOffset increments between business quarter dates for 52-53 week fiscal year (also known as a 4-4-5 calendar). It is used by companies that desire that their fiscal year always end on the same day of the week. It is a method of managing accounting periods. It is a common calendar structure for some industries, such as retail, manufacturing and parking industry. For more information see: https://en.wikipedia.org/wiki/4-4-5_calendar The year may either:  end on the last X day of the Y month. end on the last X day closest to the last day of the Y month.  X is a specific day of the week. Y is a certain month of the year startingMonth = 1 corresponds to dates like 1/31/2007, 4/30/2007, … startingMonth = 2 corresponds to dates like 2/28/2007, 5/31/2007, … startingMonth = 3 corresponds to dates like 3/30/2007, 6/29/2007, …  Parameters

n:int

weekday:int {0, 1, …, 6}, default 0

A specific integer for the day of the week.  0 is Monday 1 is Tuesday 2 is Wednesday 3 is Thursday 4 is Friday 5 is Saturday 6 is Sunday.
startingMonth:int {1, 2, …, 12}, default 1

The month in which fiscal years end.
qtr_with_extra_week:int {1, 2, 3, 4}, default 1

The quarter number that has the leap or 14 week when needed.
variation:str, default “nearest”

Method of employing 4-4-5 calendar. There are two options:  “nearest” means year end is weekday closest to last day of month in year. “last” means year end is final weekday of the final month in fiscal year.      Attributes
base Returns a copy of the calling offset object with n=1 and all other attributes equal.
freqstr
kwds
n
name
nanos
normalize
qtr_with_extra_week
rule_code
startingMonth
variation
weekday     Methods
__call__(*args, **kwargs) Call self as a function.
rollback Roll provided date backward to next offset only if not on offset.
rollforward Roll provided date forward to next offset only if not on offset.
apply
apply_index
copy
get_rule_code_suffix
get_weeks
isAnchored
is_anchored
is_month_end
is_month_start
is_on_offset
is_quarter_end
is_quarter_start
is_year_end
is_year_start
onOffset
year_has_extra_week


pandas.tseries.offsets.FY5253Quarter.__call__   FY5253Quarter.__call__(*args, **kwargs)

Call self as a function.


pandas.tseries.offsets.FY5253Quarter.apply   FY5253Quarter.apply()


pandas.tseries.offsets.FY5253Quarter.apply_index   FY5253Quarter.apply_index(other)


pandas.tseries.offsets.FY5253Quarter.base   FY5253Quarter.base

Returns a copy of the calling offset object with n=1 and all other attributes equal.


pandas.tseries.offsets.FY5253Quarter.copy   FY5253Quarter.copy()


pandas.tseries.offsets.FY5253Quarter.freqstr   FY5253Quarter.freqstr


pandas.tseries.offsets.FY5253Quarter.get_rule_code_suffix   FY5253Quarter.get_rule_code_suffix()


pandas.tseries.offsets.FY5253Quarter.get_weeks   FY5253Quarter.get_weeks()


pandas.tseries.offsets.FY5253Quarter.is_anchored   FY5253Quarter.is_anchored()


pandas.tseries.offsets.FY5253Quarter.is_month_end   FY5253Quarter.is_month_end()


pandas.tseries.offsets.FY5253Quarter.is_month_start   FY5253Quarter.is_month_start()


pandas.tseries.offsets.FY5253Quarter.is_on_offset   FY5253Quarter.is_on_offset()


pandas.tseries.offsets.FY5253Quarter.is_quarter_end   FY5253Quarter.is_quarter_end()


pandas.tseries.offsets.FY5253Quarter.is_quarter_start   FY5253Quarter.is_quarter_start()


pandas.tseries.offsets.FY5253Quarter.is_year_end   FY5253Quarter.is_year_end()


pandas.tseries.offsets.FY5253Quarter.is_year_start   FY5253Quarter.is_year_start()


pandas.tseries.offsets.FY5253Quarter.isAnchored   FY5253Quarter.isAnchored()


pandas.tseries.offsets.FY5253Quarter.kwds   FY5253Quarter.kwds


pandas.tseries.offsets.FY5253Quarter.n   FY5253Quarter.n


pandas.tseries.offsets.FY5253Quarter.name   FY5253Quarter.name


pandas.tseries.offsets.FY5253Quarter.nanos   FY5253Quarter.nanos


pandas.tseries.offsets.FY5253Quarter.normalize   FY5253Quarter.normalize


pandas.tseries.offsets.FY5253Quarter.onOffset   FY5253Quarter.onOffset()


pandas.tseries.offsets.FY5253Quarter.qtr_with_extra_week   FY5253Quarter.qtr_with_extra_week


pandas.tseries.offsets.FY5253Quarter.rollback   FY5253Quarter.rollback()

Roll provided date backward to next offset only if not on offset.  Returns
 TimeStamp

Rolled timestamp if not on offset, otherwise unchanged timestamp.


pandas.tseries.offsets.FY5253Quarter.rollforward   FY5253Quarter.rollforward()

Roll provided date forward to next offset only if not on offset.  Returns
 TimeStamp

Rolled timestamp if not on offset, otherwise unchanged timestamp.


pandas.tseries.offsets.FY5253Quarter.rule_code   FY5253Quarter.rule_code


pandas.tseries.offsets.FY5253Quarter.startingMonth   FY5253Quarter.startingMonth


pandas.tseries.offsets.FY5253Quarter.variation   FY5253Quarter.variation


pandas.tseries.offsets.FY5253Quarter.weekday   FY5253Quarter.weekday


pandas.tseries.offsets.FY5253Quarter.year_has_extra_week   FY5253Quarter.year_has_extra_week()


pandas.tseries.offsets.Hour   classpandas.tseries.offsets.Hour

Attributes
base Returns a copy of the calling offset object with n=1 and all other attributes equal.
delta
freqstr
kwds
n
name
nanos
normalize
rule_code     Methods
__call__(*args, **kwargs) Call self as a function.
rollback Roll provided date backward to next offset only if not on offset.
rollforward Roll provided date forward to next offset only if not on offset.
apply
apply_index
copy
isAnchored
is_anchored
is_month_end
is_month_start
is_on_offset
is_quarter_end
is_quarter_start
is_year_end
is_year_start
onOffset


pandas.tseries.offsets.Hour.__call__   Hour.__call__(*args, **kwargs)

Call self as a function.


pandas.tseries.offsets.Hour.apply   Hour.apply()


pandas.tseries.offsets.Hour.apply_index   Hour.apply_index(other)


pandas.tseries.offsets.Hour.base   Hour.base

Returns a copy of the calling offset object with n=1 and all other attributes equal.


pandas.tseries.offsets.Hour.copy   Hour.copy()


pandas.tseries.offsets.Hour.delta   Hour.delta


pandas.tseries.offsets.Hour.freqstr   Hour.freqstr


pandas.tseries.offsets.Hour.is_anchored   Hour.is_anchored()


pandas.tseries.offsets.Hour.is_month_end   Hour.is_month_end()


pandas.tseries.offsets.Hour.is_month_start   Hour.is_month_start()


pandas.tseries.offsets.Hour.is_on_offset   Hour.is_on_offset()


pandas.tseries.offsets.Hour.is_quarter_end   Hour.is_quarter_end()


pandas.tseries.offsets.Hour.is_quarter_start   Hour.is_quarter_start()


pandas.tseries.offsets.Hour.is_year_end   Hour.is_year_end()


pandas.tseries.offsets.Hour.is_year_start   Hour.is_year_start()


pandas.tseries.offsets.Hour.isAnchored   Hour.isAnchored()


pandas.tseries.offsets.Hour.kwds   Hour.kwds


pandas.tseries.offsets.Hour.name   Hour.name


pandas.tseries.offsets.Hour.nanos   Hour.nanos


pandas.tseries.offsets.Hour.normalize   Hour.normalize


pandas.tseries.offsets.Hour.onOffset   Hour.onOffset()


pandas.tseries.offsets.Hour.rollback   Hour.rollback()

Roll provided date backward to next offset only if not on offset.  Returns
 TimeStamp

Rolled timestamp if not on offset, otherwise unchanged timestamp.


pandas.tseries.offsets.Hour.rollforward   Hour.rollforward()

Roll provided date forward to next offset only if not on offset.  Returns
 TimeStamp

Rolled timestamp if not on offset, otherwise unchanged timestamp.


pandas.tseries.offsets.Hour.rule_code   Hour.rule_code


pandas.tseries.offsets.LastWeekOfMonth   classpandas.tseries.offsets.LastWeekOfMonth

Describes monthly dates in last week of month like “the last Tuesday of each month”.  Parameters

n:int, default 1

weekday:int {0, 1, …, 6}, default 0

A specific integer for the day of the week.  0 is Monday 1 is Tuesday 2 is Wednesday 3 is Thursday 4 is Friday 5 is Saturday 6 is Sunday.      Attributes
base Returns a copy of the calling offset object with n=1 and all other attributes equal.
freqstr
kwds
n
name
nanos
normalize
rule_code
week
weekday     Methods
__call__(*args, **kwargs) Call self as a function.
rollback Roll provided date backward to next offset only if not on offset.
rollforward Roll provided date forward to next offset only if not on offset.
apply
apply_index
copy
isAnchored
is_anchored
is_month_end
is_month_start
is_on_offset
is_quarter_end
is_quarter_start
is_year_end
is_year_start
onOffset


pandas.tseries.offsets.LastWeekOfMonth.__call__   LastWeekOfMonth.__call__(*args, **kwargs)

Call self as a function.


pandas.tseries.offsets.LastWeekOfMonth.apply   LastWeekOfMonth.apply()


pandas.tseries.offsets.LastWeekOfMonth.apply_index   LastWeekOfMonth.apply_index(other)


pandas.tseries.offsets.LastWeekOfMonth.base   LastWeekOfMonth.base

Returns a copy of the calling offset object with n=1 and all other attributes equal.


pandas.tseries.offsets.LastWeekOfMonth.copy   LastWeekOfMonth.copy()


pandas.tseries.offsets.LastWeekOfMonth.freqstr   LastWeekOfMonth.freqstr


pandas.tseries.offsets.LastWeekOfMonth.is_anchored   LastWeekOfMonth.is_anchored()


pandas.tseries.offsets.LastWeekOfMonth.is_month_end   LastWeekOfMonth.is_month_end()


pandas.tseries.offsets.LastWeekOfMonth.is_month_start   LastWeekOfMonth.is_month_start()


pandas.tseries.offsets.LastWeekOfMonth.is_on_offset   LastWeekOfMonth.is_on_offset()


pandas.tseries.offsets.LastWeekOfMonth.is_quarter_end   LastWeekOfMonth.is_quarter_end()


pandas.tseries.offsets.LastWeekOfMonth.is_quarter_start   LastWeekOfMonth.is_quarter_start()


pandas.tseries.offsets.LastWeekOfMonth.is_year_end   LastWeekOfMonth.is_year_end()


pandas.tseries.offsets.LastWeekOfMonth.is_year_start   LastWeekOfMonth.is_year_start()


pandas.tseries.offsets.LastWeekOfMonth.isAnchored   LastWeekOfMonth.isAnchored()


pandas.tseries.offsets.LastWeekOfMonth.kwds   LastWeekOfMonth.kwds


pandas.tseries.offsets.LastWeekOfMonth.n   LastWeekOfMonth.n


pandas.tseries.offsets.LastWeekOfMonth.name   LastWeekOfMonth.name


pandas.tseries.offsets.LastWeekOfMonth.nanos   LastWeekOfMonth.nanos


pandas.tseries.offsets.LastWeekOfMonth.normalize   LastWeekOfMonth.normalize


pandas.tseries.offsets.LastWeekOfMonth.onOffset   LastWeekOfMonth.onOffset()


pandas.tseries.offsets.LastWeekOfMonth.rollback   LastWeekOfMonth.rollback()

Roll provided date backward to next offset only if not on offset.  Returns
 TimeStamp

Rolled timestamp if not on offset, otherwise unchanged timestamp.


pandas.tseries.offsets.LastWeekOfMonth.rollforward   LastWeekOfMonth.rollforward()

Roll provided date forward to next offset only if not on offset.  Returns
 TimeStamp

Rolled timestamp if not on offset, otherwise unchanged timestamp.


pandas.tseries.offsets.LastWeekOfMonth.rule_code   LastWeekOfMonth.rule_code


pandas.tseries.offsets.LastWeekOfMonth.week   LastWeekOfMonth.week


pandas.tseries.offsets.LastWeekOfMonth.weekday   LastWeekOfMonth.weekday


pandas.tseries.offsets.Micro   classpandas.tseries.offsets.Micro

Attributes
base Returns a copy of the calling offset object with n=1 and all other attributes equal.
delta
freqstr
kwds
n
name
nanos
normalize
rule_code     Methods
__call__(*args, **kwargs) Call self as a function.
rollback Roll provided date backward to next offset only if not on offset.
rollforward Roll provided date forward to next offset only if not on offset.
apply
apply_index
copy
isAnchored
is_anchored
is_month_end
is_month_start
is_on_offset
is_quarter_end
is_quarter_start
is_year_end
is_year_start
onOffset


pandas.tseries.offsets.Micro.__call__   Micro.__call__(*args, **kwargs)

Call self as a function.


pandas.tseries.offsets.Micro.apply   Micro.apply()


pandas.tseries.offsets.Micro.apply_index   Micro.apply_index(other)


pandas.tseries.offsets.Micro.base   Micro.base

Returns a copy of the calling offset object with n=1 and all other attributes equal.


pandas.tseries.offsets.Micro.copy   Micro.copy()


pandas.tseries.offsets.Micro.delta   Micro.delta


pandas.tseries.offsets.Micro.freqstr   Micro.freqstr


pandas.tseries.offsets.Micro.is_anchored   Micro.is_anchored()


pandas.tseries.offsets.Micro.is_month_end   Micro.is_month_end()


pandas.tseries.offsets.Micro.is_month_start   Micro.is_month_start()


pandas.tseries.offsets.Micro.is_on_offset   Micro.is_on_offset()


pandas.tseries.offsets.Micro.is_quarter_end   Micro.is_quarter_end()


pandas.tseries.offsets.Micro.is_quarter_start   Micro.is_quarter_start()


pandas.tseries.offsets.Micro.is_year_end   Micro.is_year_end()


pandas.tseries.offsets.Micro.is_year_start   Micro.is_year_start()


pandas.tseries.offsets.Micro.isAnchored   Micro.isAnchored()


pandas.tseries.offsets.Micro.kwds   Micro.kwds


pandas.tseries.offsets.Micro.n   Micro.n


pandas.tseries.offsets.Micro.name   Micro.name


pandas.tseries.offsets.Micro.nanos   Micro.nanos


pandas.tseries.offsets.Micro.normalize   Micro.normalize


pandas.tseries.offsets.Micro.onOffset   Micro.onOffset()


pandas.tseries.offsets.Micro.rollback   Micro.rollback()

Roll provided date backward to next offset only if not on offset.  Returns
 TimeStamp

Rolled timestamp if not on offset, otherwise unchanged timestamp.


pandas.tseries.offsets.Micro.rollforward   Micro.rollforward()

Roll provided date forward to next offset only if not on offset.  Returns
 TimeStamp

Rolled timestamp if not on offset, otherwise unchanged timestamp.


pandas.tseries.offsets.Micro.rule_code   Micro.rule_code


pandas.tseries.offsets.Milli   classpandas.tseries.offsets.Milli

Attributes
base Returns a copy of the calling offset object with n=1 and all other attributes equal.
delta
freqstr
kwds
n
name
nanos
normalize
rule_code     Methods
__call__(*args, **kwargs) Call self as a function.
rollback Roll provided date backward to next offset only if not on offset.
rollforward Roll provided date forward to next offset only if not on offset.
apply
apply_index
copy
isAnchored
is_anchored
is_month_end
is_month_start
is_on_offset
is_quarter_end
is_quarter_start
is_year_end
is_year_start
onOffset


pandas.tseries.offsets.Milli.__call__   Milli.__call__(*args, **kwargs)

Call self as a function.


pandas.tseries.offsets.Milli.apply   Milli.apply()


pandas.tseries.offsets.Milli.apply_index   Milli.apply_index(other)


pandas.tseries.offsets.Milli.base   Milli.base

Returns a copy of the calling offset object with n=1 and all other attributes equal.


pandas.tseries.offsets.Milli.copy   Milli.copy()


pandas.tseries.offsets.Milli.delta   Milli.delta


pandas.tseries.offsets.Milli.freqstr   Milli.freqstr


pandas.tseries.offsets.Milli.is_anchored   Milli.is_anchored()


pandas.tseries.offsets.Milli.is_month_end   Milli.is_month_end()


pandas.tseries.offsets.Milli.is_month_start   Milli.is_month_start()


pandas.tseries.offsets.Milli.is_on_offset   Milli.is_on_offset()


pandas.tseries.offsets.Milli.is_quarter_end   Milli.is_quarter_end()


pandas.tseries.offsets.Milli.is_quarter_start   Milli.is_quarter_start()


pandas.tseries.offsets.Milli.is_year_end   Milli.is_year_end()


pandas.tseries.offsets.Milli.is_year_start   Milli.is_year_start()


pandas.tseries.offsets.Milli.isAnchored   Milli.isAnchored()


pandas.tseries.offsets.Milli.kwds   Milli.kwds


pandas.tseries.offsets.Milli.n   Milli.n


pandas.tseries.offsets.Milli.name   Milli.name


pandas.tseries.offsets.Milli.nanos   Milli.nanos


pandas.tseries.offsets.Milli.normalize   Milli.normalize


pandas.tseries.offsets.Milli.onOffset   Milli.onOffset()


pandas.tseries.offsets.Milli.rollback   Milli.rollback()

Roll provided date backward to next offset only if not on offset.  Returns
 TimeStamp

Rolled timestamp if not on offset, otherwise unchanged timestamp.


pandas.tseries.offsets.Milli.rollforward   Milli.rollforward()

Roll provided date forward to next offset only if not on offset.  Returns
 TimeStamp

Rolled timestamp if not on offset, otherwise unchanged timestamp.


pandas.tseries.offsets.Milli.rule_code   Milli.rule_code


pandas.tseries.offsets.Minute   classpandas.tseries.offsets.Minute

Attributes
base Returns a copy of the calling offset object with n=1 and all other attributes equal.
delta
freqstr
kwds
n
name
nanos
normalize
rule_code     Methods
__call__(*args, **kwargs) Call self as a function.
rollback Roll provided date backward to next offset only if not on offset.
rollforward Roll provided date forward to next offset only if not on offset.
apply
apply_index
copy
isAnchored
is_anchored
is_month_end
is_month_start
is_on_offset
is_quarter_end
is_quarter_start
is_year_end
is_year_start
onOffset


pandas.tseries.offsets.Minute.__call__   Minute.__call__(*args, **kwargs)

Call self as a function.


pandas.tseries.offsets.Minute.apply   Minute.apply()


pandas.tseries.offsets.Minute.apply_index   Minute.apply_index(other)


pandas.tseries.offsets.Minute.base   Minute.base

Returns a copy of the calling offset object with n=1 and all other attributes equal.


pandas.tseries.offsets.Minute.copy   Minute.copy()


pandas.tseries.offsets.Minute.delta   Minute.delta


pandas.tseries.offsets.Minute.freqstr   Minute.freqstr


pandas.tseries.offsets.Minute.is_anchored   Minute.is_anchored()


pandas.tseries.offsets.Minute.is_month_end   Minute.is_month_end()


pandas.tseries.offsets.Minute.is_month_start   Minute.is_month_start()


pandas.tseries.offsets.Minute.is_on_offset   Minute.is_on_offset()


pandas.tseries.offsets.Minute.is_quarter_end   Minute.is_quarter_end()


pandas.tseries.offsets.Minute.is_quarter_start   Minute.is_quarter_start()


pandas.tseries.offsets.Minute.is_year_end   Minute.is_year_end()


pandas.tseries.offsets.Minute.is_year_start   Minute.is_year_start()


pandas.tseries.offsets.Minute.isAnchored   Minute.isAnchored()


pandas.tseries.offsets.Minute.kwds   Minute.kwds


pandas.tseries.offsets.Minute.n   Minute.n


pandas.tseries.offsets.Minute.name   Minute.name


pandas.tseries.offsets.Minute.nanos   Minute.nanos


pandas.tseries.offsets.Minute.normalize   Minute.normalize


pandas.tseries.offsets.Minute.onOffset   Minute.onOffset()


pandas.tseries.offsets.Minute.rollback   Minute.rollback()

Roll provided date backward to next offset only if not on offset.  Returns
 TimeStamp

Rolled timestamp if not on offset, otherwise unchanged timestamp.


pandas.tseries.offsets.Minute.rollforward   Minute.rollforward()

Roll provided date forward to next offset only if not on offset.  Returns
 TimeStamp

Rolled timestamp if not on offset, otherwise unchanged timestamp.


pandas.tseries.offsets.Minute.rule_code   Minute.rule_code


pandas.tseries.offsets.MonthBegin   classpandas.tseries.offsets.MonthBegin

DateOffset of one month at beginning. Attributes
base Returns a copy of the calling offset object with n=1 and all other attributes equal.
freqstr
kwds
n
name
nanos
normalize
rule_code     Methods
__call__(*args, **kwargs) Call self as a function.
rollback Roll provided date backward to next offset only if not on offset.
rollforward Roll provided date forward to next offset only if not on offset.
apply
apply_index
copy
isAnchored
is_anchored
is_month_end
is_month_start
is_on_offset
is_quarter_end
is_quarter_start
is_year_end
is_year_start
onOffset


pandas.tseries.offsets.MonthBegin.__call__   MonthBegin.__call__(*args, **kwargs)

Call self as a function.


pandas.tseries.offsets.MonthBegin.apply   MonthBegin.apply()


pandas.tseries.offsets.MonthBegin.apply_index   MonthBegin.apply_index(other)


pandas.tseries.offsets.MonthBegin.base   MonthBegin.base

Returns a copy of the calling offset object with n=1 and all other attributes equal.


pandas.tseries.offsets.MonthBegin.copy   MonthBegin.copy()


pandas.tseries.offsets.MonthBegin.freqstr   MonthBegin.freqstr


pandas.tseries.offsets.MonthBegin.is_anchored   MonthBegin.is_anchored()


pandas.tseries.offsets.MonthBegin.is_month_end   MonthBegin.is_month_end()


pandas.tseries.offsets.MonthBegin.is_month_start   MonthBegin.is_month_start()


pandas.tseries.offsets.MonthBegin.is_on_offset   MonthBegin.is_on_offset()


pandas.tseries.offsets.MonthBegin.is_quarter_end   MonthBegin.is_quarter_end()


pandas.tseries.offsets.MonthBegin.is_quarter_start   MonthBegin.is_quarter_start()


pandas.tseries.offsets.MonthBegin.is_year_end   MonthBegin.is_year_end()


pandas.tseries.offsets.MonthBegin.is_year_start   MonthBegin.is_year_start()


pandas.tseries.offsets.MonthBegin.isAnchored   MonthBegin.isAnchored()


pandas.tseries.offsets.MonthBegin.kwds   MonthBegin.kwds


pandas.tseries.offsets.MonthBegin.n   MonthBegin.n


pandas.tseries.offsets.MonthBegin.name   MonthBegin.name


pandas.tseries.offsets.MonthBegin.nanos   MonthBegin.nanos


pandas.tseries.offsets.MonthBegin.normalize   MonthBegin.normalize


pandas.tseries.offsets.MonthBegin.onOffset   MonthBegin.onOffset()


pandas.tseries.offsets.MonthBegin.rollback   MonthBegin.rollback()

Roll provided date backward to next offset only if not on offset.  Returns
 TimeStamp

Rolled timestamp if not on offset, otherwise unchanged timestamp.


pandas.tseries.offsets.MonthBegin.rollforward   MonthBegin.rollforward()

Roll provided date forward to next offset only if not on offset.  Returns
 TimeStamp

Rolled timestamp if not on offset, otherwise unchanged timestamp.


pandas.tseries.offsets.MonthBegin.rule_code   MonthBegin.rule_code


pandas.tseries.offsets.MonthEnd   classpandas.tseries.offsets.MonthEnd

DateOffset of one month end. Attributes
base Returns a copy of the calling offset object with n=1 and all other attributes equal.
freqstr
kwds
n
name
nanos
normalize
rule_code     Methods
__call__(*args, **kwargs) Call self as a function.
rollback Roll provided date backward to next offset only if not on offset.
rollforward Roll provided date forward to next offset only if not on offset.
apply
apply_index
copy
isAnchored
is_anchored
is_month_end
is_month_start
is_on_offset
is_quarter_end
is_quarter_start
is_year_end
is_year_start
onOffset


pandas.tseries.offsets.MonthEnd.__call__   MonthEnd.__call__(*args, **kwargs)

Call self as a function.


pandas.tseries.offsets.MonthEnd.apply   MonthEnd.apply()


pandas.tseries.offsets.MonthEnd.apply_index   MonthEnd.apply_index(other)


pandas.tseries.offsets.MonthEnd.base   MonthEnd.base

Returns a copy of the calling offset object with n=1 and all other attributes equal.


pandas.tseries.offsets.MonthEnd.copy   MonthEnd.copy()


pandas.tseries.offsets.MonthEnd.freqstr   MonthEnd.freqstr


pandas.tseries.offsets.MonthEnd.is_anchored   MonthEnd.is_anchored()


pandas.tseries.offsets.MonthEnd.is_month_end   MonthEnd.is_month_end()


pandas.tseries.offsets.MonthEnd.is_month_start   MonthEnd.is_month_start()


pandas.tseries.offsets.MonthEnd.is_on_offset   MonthEnd.is_on_offset()


pandas.tseries.offsets.MonthEnd.is_quarter_end   MonthEnd.is_quarter_end()


pandas.tseries.offsets.MonthEnd.is_quarter_start   MonthEnd.is_quarter_start()


pandas.tseries.offsets.MonthEnd.is_year_end   MonthEnd.is_year_end()


pandas.tseries.offsets.MonthEnd.is_year_start   MonthEnd.is_year_start()


pandas.tseries.offsets.MonthEnd.isAnchored   MonthEnd.isAnchored()


pandas.tseries.offsets.MonthEnd.kwds   MonthEnd.kwds


pandas.tseries.offsets.MonthEnd.n   MonthEnd.n


pandas.tseries.offsets.MonthEnd.name   MonthEnd.name


pandas.tseries.offsets.MonthEnd.nanos   MonthEnd.nanos


pandas.tseries.offsets.MonthEnd.normalize   MonthEnd.normalize


pandas.tseries.offsets.MonthEnd.onOffset   MonthEnd.onOffset()


pandas.tseries.offsets.MonthEnd.rollback   MonthEnd.rollback()

Roll provided date backward to next offset only if not on offset.  Returns
 TimeStamp

Rolled timestamp if not on offset, otherwise unchanged timestamp.


pandas.tseries.offsets.MonthEnd.rollforward   MonthEnd.rollforward()

Roll provided date forward to next offset only if not on offset.  Returns
 TimeStamp

Rolled timestamp if not on offset, otherwise unchanged timestamp.


pandas.tseries.offsets.MonthEnd.rule_code   MonthEnd.rule_code


pandas.tseries.offsets.Nano   classpandas.tseries.offsets.Nano

Attributes
base Returns a copy of the calling offset object with n=1 and all other attributes equal.
delta
freqstr
kwds
n
name
nanos
normalize
rule_code     Methods
__call__(*args, **kwargs) Call self as a function.
rollback Roll provided date backward to next offset only if not on offset.
rollforward Roll provided date forward to next offset only if not on offset.
apply
apply_index
copy
isAnchored
is_anchored
is_month_end
is_month_start
is_on_offset
is_quarter_end
is_quarter_start
is_year_end
is_year_start
onOffset


pandas.tseries.offsets.Nano.__call__   Nano.__call__(*args, **kwargs)

Call self as a function.


pandas.tseries.offsets.Nano.apply   Nano.apply()


pandas.tseries.offsets.Nano.apply_index   Nano.apply_index(other)


pandas.tseries.offsets.Nano.base   Nano.base

Returns a copy of the calling offset object with n=1 and all other attributes equal.


pandas.tseries.offsets.Nano.copy   Nano.copy()


pandas.tseries.offsets.Nano.delta   Nano.delta


pandas.tseries.offsets.Nano.freqstr   Nano.freqstr


pandas.tseries.offsets.Nano.is_anchored   Nano.is_anchored()


pandas.tseries.offsets.Nano.is_month_end   Nano.is_month_end()


pandas.tseries.offsets.Nano.is_month_start   Nano.is_month_start()


pandas.tseries.offsets.Nano.is_on_offset   Nano.is_on_offset()


pandas.tseries.offsets.Nano.is_quarter_end   Nano.is_quarter_end()


pandas.tseries.offsets.Nano.is_quarter_start   Nano.is_quarter_start()


pandas.tseries.offsets.Nano.is_year_end   Nano.is_year_end()


pandas.tseries.offsets.Nano.is_year_start   Nano.is_year_start()


pandas.tseries.offsets.Nano.isAnchored   Nano.isAnchored()


pandas.tseries.offsets.Nano.kwds   Nano.kwds


pandas.tseries.offsets.Nano.name   Nano.name


pandas.tseries.offsets.Nano.nanos   Nano.nanos


pandas.tseries.offsets.Nano.normalize   Nano.normalize


pandas.tseries.offsets.Nano.onOffset   Nano.onOffset()


pandas.tseries.offsets.Nano.rollback   Nano.rollback()

Roll provided date backward to next offset only if not on offset.  Returns
 TimeStamp

Rolled timestamp if not on offset, otherwise unchanged timestamp.


pandas.tseries.offsets.Nano.rollforward   Nano.rollforward()

Roll provided date forward to next offset only if not on offset.  Returns
 TimeStamp

Rolled timestamp if not on offset, otherwise unchanged timestamp.


pandas.tseries.offsets.Nano.rule_code   Nano.rule_code


pandas.tseries.offsets.QuarterBegin   classpandas.tseries.offsets.QuarterBegin

DateOffset increments between Quarter start dates. startingMonth = 1 corresponds to dates like 1/01/2007, 4/01/2007, … startingMonth = 2 corresponds to dates like 2/01/2007, 5/01/2007, … startingMonth = 3 corresponds to dates like 3/01/2007, 6/01/2007, … Attributes
base Returns a copy of the calling offset object with n=1 and all other attributes equal.
freqstr
kwds
n
name
nanos
normalize
rule_code
startingMonth     Methods
__call__(*args, **kwargs) Call self as a function.
rollback Roll provided date backward to next offset only if not on offset.
rollforward Roll provided date forward to next offset only if not on offset.
apply
apply_index
copy
isAnchored
is_anchored
is_month_end
is_month_start
is_on_offset
is_quarter_end
is_quarter_start
is_year_end
is_year_start
onOffset


pandas.tseries.offsets.QuarterBegin.__call__   QuarterBegin.__call__(*args, **kwargs)

Call self as a function.


pandas.tseries.offsets.QuarterBegin.apply   QuarterBegin.apply()


pandas.tseries.offsets.QuarterBegin.apply_index   QuarterBegin.apply_index(other)


pandas.tseries.offsets.QuarterBegin.base   QuarterBegin.base

Returns a copy of the calling offset object with n=1 and all other attributes equal.


pandas.tseries.offsets.QuarterBegin.copy   QuarterBegin.copy()


pandas.tseries.offsets.QuarterBegin.freqstr   QuarterBegin.freqstr


pandas.tseries.offsets.QuarterBegin.is_anchored   QuarterBegin.is_anchored()


pandas.tseries.offsets.QuarterBegin.is_month_end   QuarterBegin.is_month_end()


pandas.tseries.offsets.QuarterBegin.is_month_start   QuarterBegin.is_month_start()


pandas.tseries.offsets.QuarterBegin.is_on_offset   QuarterBegin.is_on_offset()


pandas.tseries.offsets.QuarterBegin.is_quarter_end   QuarterBegin.is_quarter_end()


pandas.tseries.offsets.QuarterBegin.is_quarter_start   QuarterBegin.is_quarter_start()


pandas.tseries.offsets.QuarterBegin.is_year_end   QuarterBegin.is_year_end()


pandas.tseries.offsets.QuarterBegin.is_year_start   QuarterBegin.is_year_start()


pandas.tseries.offsets.QuarterBegin.isAnchored   QuarterBegin.isAnchored()


pandas.tseries.offsets.QuarterBegin.kwds   QuarterBegin.kwds


pandas.tseries.offsets.QuarterBegin.n   QuarterBegin.n


pandas.tseries.offsets.QuarterBegin.name   QuarterBegin.name


pandas.tseries.offsets.QuarterBegin.nanos   QuarterBegin.nanos


pandas.tseries.offsets.QuarterBegin.normalize   QuarterBegin.normalize


pandas.tseries.offsets.QuarterBegin.onOffset   QuarterBegin.onOffset()


pandas.tseries.offsets.QuarterBegin.rollback   QuarterBegin.rollback()

Roll provided date backward to next offset only if not on offset.  Returns
 TimeStamp

Rolled timestamp if not on offset, otherwise unchanged timestamp.


pandas.tseries.offsets.QuarterBegin.rollforward   QuarterBegin.rollforward()

Roll provided date forward to next offset only if not on offset.  Returns
 TimeStamp

Rolled timestamp if not on offset, otherwise unchanged timestamp.


pandas.tseries.offsets.QuarterBegin.rule_code   QuarterBegin.rule_code


pandas.tseries.offsets.QuarterBegin.startingMonth   QuarterBegin.startingMonth


pandas.tseries.offsets.QuarterEnd   classpandas.tseries.offsets.QuarterEnd

DateOffset increments between Quarter end dates. startingMonth = 1 corresponds to dates like 1/31/2007, 4/30/2007, … startingMonth = 2 corresponds to dates like 2/28/2007, 5/31/2007, … startingMonth = 3 corresponds to dates like 3/31/2007, 6/30/2007, … Attributes
base Returns a copy of the calling offset object with n=1 and all other attributes equal.
freqstr
kwds
n
name
nanos
normalize
rule_code
startingMonth     Methods
__call__(*args, **kwargs) Call self as a function.
rollback Roll provided date backward to next offset only if not on offset.
rollforward Roll provided date forward to next offset only if not on offset.
apply
apply_index
copy
isAnchored
is_anchored
is_month_end
is_month_start
is_on_offset
is_quarter_end
is_quarter_start
is_year_end
is_year_start
onOffset


pandas.tseries.offsets.QuarterEnd.__call__   QuarterEnd.__call__(*args, **kwargs)

Call self as a function.


pandas.tseries.offsets.QuarterEnd.apply   QuarterEnd.apply()


pandas.tseries.offsets.QuarterEnd.apply_index   QuarterEnd.apply_index(other)


pandas.tseries.offsets.QuarterEnd.base   QuarterEnd.base

Returns a copy of the calling offset object with n=1 and all other attributes equal.


pandas.tseries.offsets.QuarterEnd.copy   QuarterEnd.copy()


pandas.tseries.offsets.QuarterEnd.freqstr   QuarterEnd.freqstr


pandas.tseries.offsets.QuarterEnd.is_anchored   QuarterEnd.is_anchored()


pandas.tseries.offsets.QuarterEnd.is_month_end   QuarterEnd.is_month_end()


pandas.tseries.offsets.QuarterEnd.is_month_start   QuarterEnd.is_month_start()


pandas.tseries.offsets.QuarterEnd.is_on_offset   QuarterEnd.is_on_offset()


pandas.tseries.offsets.QuarterEnd.is_quarter_end   QuarterEnd.is_quarter_end()


pandas.tseries.offsets.QuarterEnd.is_quarter_start   QuarterEnd.is_quarter_start()


pandas.tseries.offsets.QuarterEnd.is_year_end   QuarterEnd.is_year_end()


pandas.tseries.offsets.QuarterEnd.is_year_start   QuarterEnd.is_year_start()


pandas.tseries.offsets.QuarterEnd.isAnchored   QuarterEnd.isAnchored()


pandas.tseries.offsets.QuarterEnd.kwds   QuarterEnd.kwds


pandas.tseries.offsets.QuarterEnd.n   QuarterEnd.n


pandas.tseries.offsets.QuarterEnd.name   QuarterEnd.name


pandas.tseries.offsets.QuarterEnd.nanos   QuarterEnd.nanos


pandas.tseries.offsets.QuarterEnd.normalize   QuarterEnd.normalize


pandas.tseries.offsets.QuarterEnd.onOffset   QuarterEnd.onOffset()


pandas.tseries.offsets.QuarterEnd.rollback   QuarterEnd.rollback()

Roll provided date backward to next offset only if not on offset.  Returns
 TimeStamp

Rolled timestamp if not on offset, otherwise unchanged timestamp.


pandas.tseries.offsets.QuarterEnd.rollforward   QuarterEnd.rollforward()

Roll provided date forward to next offset only if not on offset.  Returns
 TimeStamp

Rolled timestamp if not on offset, otherwise unchanged timestamp.


pandas.tseries.offsets.QuarterEnd.rule_code   QuarterEnd.rule_code


pandas.tseries.offsets.QuarterEnd.startingMonth   QuarterEnd.startingMonth


pandas.tseries.offsets.Second   classpandas.tseries.offsets.Second

Attributes
base Returns a copy of the calling offset object with n=1 and all other attributes equal.
delta
freqstr
kwds
n
name
nanos
normalize
rule_code     Methods
__call__(*args, **kwargs) Call self as a function.
rollback Roll provided date backward to next offset only if not on offset.
rollforward Roll provided date forward to next offset only if not on offset.
apply
apply_index
copy
isAnchored
is_anchored
is_month_end
is_month_start
is_on_offset
is_quarter_end
is_quarter_start
is_year_end
is_year_start
onOffset


pandas.tseries.offsets.Second.__call__   Second.__call__(*args, **kwargs)

Call self as a function.


pandas.tseries.offsets.Second.apply   Second.apply()


pandas.tseries.offsets.Second.apply_index   Second.apply_index(other)


pandas.tseries.offsets.Second.base   Second.base

Returns a copy of the calling offset object with n=1 and all other attributes equal.


pandas.tseries.offsets.Second.copy   Second.copy()


pandas.tseries.offsets.Second.delta   Second.delta


pandas.tseries.offsets.Second.freqstr   Second.freqstr


pandas.tseries.offsets.Second.is_anchored   Second.is_anchored()


pandas.tseries.offsets.Second.is_month_end   Second.is_month_end()


pandas.tseries.offsets.Second.is_month_start   Second.is_month_start()


pandas.tseries.offsets.Second.is_on_offset   Second.is_on_offset()


pandas.tseries.offsets.Second.is_quarter_end   Second.is_quarter_end()


pandas.tseries.offsets.Second.is_quarter_start   Second.is_quarter_start()


pandas.tseries.offsets.Second.is_year_end   Second.is_year_end()


pandas.tseries.offsets.Second.is_year_start   Second.is_year_start()


pandas.tseries.offsets.Second.isAnchored   Second.isAnchored()


pandas.tseries.offsets.Second.kwds   Second.kwds


pandas.tseries.offsets.Second.n   Second.n


pandas.tseries.offsets.Second.name   Second.name


pandas.tseries.offsets.Second.nanos   Second.nanos


pandas.tseries.offsets.Second.normalize   Second.normalize


pandas.tseries.offsets.Second.onOffset   Second.onOffset()


pandas.tseries.offsets.Second.rollback   Second.rollback()

Roll provided date backward to next offset only if not on offset.  Returns
 TimeStamp

Rolled timestamp if not on offset, otherwise unchanged timestamp.


pandas.tseries.offsets.Second.rollforward   Second.rollforward()

Roll provided date forward to next offset only if not on offset.  Returns
 TimeStamp

Rolled timestamp if not on offset, otherwise unchanged timestamp.


pandas.tseries.offsets.Second.rule_code   Second.rule_code


pandas.tseries.offsets.SemiMonthBegin   classpandas.tseries.offsets.SemiMonthBegin

Two DateOffset’s per month repeating on the first day of the month and day_of_month.  Parameters

n:int

normalize:bool, default False

day_of_month:int, {2, 3,…,27}, default 15

   Attributes
base Returns a copy of the calling offset object with n=1 and all other attributes equal.
day_of_month
freqstr
kwds
n
name
nanos
normalize
rule_code     Methods
__call__(*args, **kwargs) Call self as a function.
rollback Roll provided date backward to next offset only if not on offset.
rollforward Roll provided date forward to next offset only if not on offset.
apply
apply_index
copy
isAnchored
is_anchored
is_month_end
is_month_start
is_on_offset
is_quarter_end
is_quarter_start
is_year_end
is_year_start
onOffset


pandas.tseries.offsets.SemiMonthBegin.__call__   SemiMonthBegin.__call__(*args, **kwargs)

Call self as a function.


pandas.tseries.offsets.SemiMonthBegin.apply   SemiMonthBegin.apply()


pandas.tseries.offsets.SemiMonthBegin.apply_index   SemiMonthBegin.apply_index(other)


pandas.tseries.offsets.SemiMonthBegin.base   SemiMonthBegin.base

Returns a copy of the calling offset object with n=1 and all other attributes equal.


pandas.tseries.offsets.SemiMonthBegin.copy   SemiMonthBegin.copy()


pandas.tseries.offsets.SemiMonthBegin.day_of_month   SemiMonthBegin.day_of_month


pandas.tseries.offsets.SemiMonthBegin.freqstr   SemiMonthBegin.freqstr


pandas.tseries.offsets.SemiMonthBegin.is_anchored   SemiMonthBegin.is_anchored()


pandas.tseries.offsets.SemiMonthBegin.is_month_end   SemiMonthBegin.is_month_end()


pandas.tseries.offsets.SemiMonthBegin.is_month_start   SemiMonthBegin.is_month_start()


pandas.tseries.offsets.SemiMonthBegin.is_on_offset   SemiMonthBegin.is_on_offset()


pandas.tseries.offsets.SemiMonthBegin.is_quarter_end   SemiMonthBegin.is_quarter_end()


pandas.tseries.offsets.SemiMonthBegin.is_quarter_start   SemiMonthBegin.is_quarter_start()


pandas.tseries.offsets.SemiMonthBegin.is_year_end   SemiMonthBegin.is_year_end()


pandas.tseries.offsets.SemiMonthBegin.is_year_start   SemiMonthBegin.is_year_start()


pandas.tseries.offsets.SemiMonthBegin.isAnchored   SemiMonthBegin.isAnchored()


pandas.tseries.offsets.SemiMonthBegin.kwds   SemiMonthBegin.kwds


pandas.tseries.offsets.SemiMonthBegin.n   SemiMonthBegin.n


pandas.tseries.offsets.SemiMonthBegin.name   SemiMonthBegin.name


pandas.tseries.offsets.SemiMonthBegin.nanos   SemiMonthBegin.nanos


pandas.tseries.offsets.SemiMonthBegin.normalize   SemiMonthBegin.normalize


pandas.tseries.offsets.SemiMonthBegin.onOffset   SemiMonthBegin.onOffset()


pandas.tseries.offsets.SemiMonthBegin.rollback   SemiMonthBegin.rollback()

Roll provided date backward to next offset only if not on offset.  Returns
 TimeStamp

Rolled timestamp if not on offset, otherwise unchanged timestamp.


pandas.tseries.offsets.SemiMonthBegin.rollforward   SemiMonthBegin.rollforward()

Roll provided date forward to next offset only if not on offset.  Returns
 TimeStamp

Rolled timestamp if not on offset, otherwise unchanged timestamp.


pandas.tseries.offsets.SemiMonthBegin.rule_code   SemiMonthBegin.rule_code


pandas.tseries.offsets.SemiMonthEnd   classpandas.tseries.offsets.SemiMonthEnd

Two DateOffset’s per month repeating on the last day of the month and day_of_month.  Parameters

n:int

normalize:bool, default False

day_of_month:int, {1, 3,…,27}, default 15

   Attributes
base Returns a copy of the calling offset object with n=1 and all other attributes equal.
day_of_month
freqstr
kwds
n
name
nanos
normalize
rule_code     Methods
__call__(*args, **kwargs) Call self as a function.
rollback Roll provided date backward to next offset only if not on offset.
rollforward Roll provided date forward to next offset only if not on offset.
apply
apply_index
copy
isAnchored
is_anchored
is_month_end
is_month_start
is_on_offset
is_quarter_end
is_quarter_start
is_year_end
is_year_start
onOffset


pandas.tseries.offsets.SemiMonthEnd.__call__   SemiMonthEnd.__call__(*args, **kwargs)

Call self as a function.


pandas.tseries.offsets.SemiMonthEnd.apply   SemiMonthEnd.apply()


pandas.tseries.offsets.SemiMonthEnd.apply_index   SemiMonthEnd.apply_index(other)


pandas.tseries.offsets.SemiMonthEnd.base   SemiMonthEnd.base

Returns a copy of the calling offset object with n=1 and all other attributes equal.


pandas.tseries.offsets.SemiMonthEnd.copy   SemiMonthEnd.copy()


pandas.tseries.offsets.SemiMonthEnd.day_of_month   SemiMonthEnd.day_of_month


pandas.tseries.offsets.SemiMonthEnd.freqstr   SemiMonthEnd.freqstr


pandas.tseries.offsets.SemiMonthEnd.is_anchored   SemiMonthEnd.is_anchored()


pandas.tseries.offsets.SemiMonthEnd.is_month_end   SemiMonthEnd.is_month_end()


pandas.tseries.offsets.SemiMonthEnd.is_month_start   SemiMonthEnd.is_month_start()


pandas.tseries.offsets.SemiMonthEnd.is_on_offset   SemiMonthEnd.is_on_offset()


pandas.tseries.offsets.SemiMonthEnd.is_quarter_end   SemiMonthEnd.is_quarter_end()


pandas.tseries.offsets.SemiMonthEnd.is_quarter_start   SemiMonthEnd.is_quarter_start()


pandas.tseries.offsets.SemiMonthEnd.is_year_end   SemiMonthEnd.is_year_end()


pandas.tseries.offsets.SemiMonthEnd.is_year_start   SemiMonthEnd.is_year_start()


pandas.tseries.offsets.SemiMonthEnd.isAnchored   SemiMonthEnd.isAnchored()


pandas.tseries.offsets.SemiMonthEnd.kwds   SemiMonthEnd.kwds


pandas.tseries.offsets.SemiMonthEnd.n   SemiMonthEnd.n


pandas.tseries.offsets.SemiMonthEnd.name   SemiMonthEnd.name


pandas.tseries.offsets.SemiMonthEnd.nanos   SemiMonthEnd.nanos


pandas.tseries.offsets.SemiMonthEnd.normalize   SemiMonthEnd.normalize


pandas.tseries.offsets.SemiMonthEnd.onOffset   SemiMonthEnd.onOffset()


pandas.tseries.offsets.SemiMonthEnd.rollback   SemiMonthEnd.rollback()

Roll provided date backward to next offset only if not on offset.  Returns
 TimeStamp

Rolled timestamp if not on offset, otherwise unchanged timestamp.


pandas.tseries.offsets.SemiMonthEnd.rollforward   SemiMonthEnd.rollforward()

Roll provided date forward to next offset only if not on offset.  Returns
 TimeStamp

Rolled timestamp if not on offset, otherwise unchanged timestamp.


pandas.tseries.offsets.SemiMonthEnd.rule_code   SemiMonthEnd.rule_code


pandas.tseries.offsets.Tick   classpandas.tseries.offsets.Tick

Attributes
base Returns a copy of the calling offset object with n=1 and all other attributes equal.
delta
freqstr
kwds
n
name
nanos
normalize
rule_code     Methods
__call__(*args, **kwargs) Call self as a function.
rollback Roll provided date backward to next offset only if not on offset.
rollforward Roll provided date forward to next offset only if not on offset.
apply
apply_index
copy
isAnchored
is_anchored
is_month_end
is_month_start
is_on_offset
is_quarter_end
is_quarter_start
is_year_end
is_year_start
onOffset


pandas.tseries.offsets.Tick.__call__   Tick.__call__(*args, **kwargs)

Call self as a function.


pandas.tseries.offsets.Tick.apply   Tick.apply()


pandas.tseries.offsets.Tick.apply_index   Tick.apply_index(other)


pandas.tseries.offsets.Tick.base   Tick.base

Returns a copy of the calling offset object with n=1 and all other attributes equal.


pandas.tseries.offsets.Tick.copy   Tick.copy()


pandas.tseries.offsets.Tick.delta   Tick.delta


pandas.tseries.offsets.Tick.freqstr   Tick.freqstr


pandas.tseries.offsets.Tick.is_anchored   Tick.is_anchored()


pandas.tseries.offsets.Tick.is_month_end   Tick.is_month_end()


pandas.tseries.offsets.Tick.is_month_start   Tick.is_month_start()


pandas.tseries.offsets.Tick.is_on_offset   Tick.is_on_offset()


pandas.tseries.offsets.Tick.is_quarter_end   Tick.is_quarter_end()


pandas.tseries.offsets.Tick.is_quarter_start   Tick.is_quarter_start()


pandas.tseries.offsets.Tick.is_year_end   Tick.is_year_end()


pandas.tseries.offsets.Tick.is_year_start   Tick.is_year_start()


pandas.tseries.offsets.Tick.isAnchored   Tick.isAnchored()


pandas.tseries.offsets.Tick.kwds   Tick.kwds


pandas.tseries.offsets.Tick.name   Tick.name


pandas.tseries.offsets.Tick.nanos   Tick.nanos


pandas.tseries.offsets.Tick.normalize   Tick.normalize


pandas.tseries.offsets.Tick.onOffset   Tick.onOffset()


pandas.tseries.offsets.Tick.rollback   Tick.rollback()

Roll provided date backward to next offset only if not on offset.  Returns
 TimeStamp

Rolled timestamp if not on offset, otherwise unchanged timestamp.


pandas.tseries.offsets.Tick.rollforward   Tick.rollforward()

Roll provided date forward to next offset only if not on offset.  Returns
 TimeStamp

Rolled timestamp if not on offset, otherwise unchanged timestamp.


pandas.tseries.offsets.Tick.rule_code   Tick.rule_code


pandas.tseries.offsets.Week   classpandas.tseries.offsets.Week

Weekly offset.  Parameters

weekday:int or None, default None

Always generate specific day of week. 0 for Monday.     Attributes
base Returns a copy of the calling offset object with n=1 and all other attributes equal.
freqstr
kwds
n
name
nanos
normalize
rule_code
weekday     Methods
__call__(*args, **kwargs) Call self as a function.
rollback Roll provided date backward to next offset only if not on offset.
rollforward Roll provided date forward to next offset only if not on offset.
apply
apply_index
copy
isAnchored
is_anchored
is_month_end
is_month_start
is_on_offset
is_quarter_end
is_quarter_start
is_year_end
is_year_start
onOffset


pandas.tseries.offsets.Week.__call__   Week.__call__(*args, **kwargs)

Call self as a function.


pandas.tseries.offsets.Week.apply   Week.apply()


pandas.tseries.offsets.Week.apply_index   Week.apply_index(other)


pandas.tseries.offsets.Week.base   Week.base

Returns a copy of the calling offset object with n=1 and all other attributes equal.


pandas.tseries.offsets.Week.copy   Week.copy()


pandas.tseries.offsets.Week.freqstr   Week.freqstr


pandas.tseries.offsets.Week.is_anchored   Week.is_anchored()


pandas.tseries.offsets.Week.is_month_end   Week.is_month_end()


pandas.tseries.offsets.Week.is_month_start   Week.is_month_start()


pandas.tseries.offsets.Week.is_on_offset   Week.is_on_offset()


pandas.tseries.offsets.Week.is_quarter_end   Week.is_quarter_end()


pandas.tseries.offsets.Week.is_quarter_start   Week.is_quarter_start()


pandas.tseries.offsets.Week.is_year_end   Week.is_year_end()


pandas.tseries.offsets.Week.is_year_start   Week.is_year_start()


pandas.tseries.offsets.Week.isAnchored   Week.isAnchored()


pandas.tseries.offsets.Week.kwds   Week.kwds


pandas.tseries.offsets.Week.name   Week.name


pandas.tseries.offsets.Week.nanos   Week.nanos


pandas.tseries.offsets.Week.normalize   Week.normalize


pandas.tseries.offsets.Week.onOffset   Week.onOffset()


pandas.tseries.offsets.Week.rollback   Week.rollback()

Roll provided date backward to next offset only if not on offset.  Returns
 TimeStamp

Rolled timestamp if not on offset, otherwise unchanged timestamp.


pandas.tseries.offsets.Week.rollforward   Week.rollforward()

Roll provided date forward to next offset only if not on offset.  Returns
 TimeStamp

Rolled timestamp if not on offset, otherwise unchanged timestamp.


pandas.tseries.offsets.Week.rule_code   Week.rule_code


pandas.tseries.offsets.Week.weekday   Week.weekday


pandas.tseries.offsets.WeekOfMonth   classpandas.tseries.offsets.WeekOfMonth

Describes monthly dates like “the Tuesday of the 2nd week of each month”.  Parameters

n:int

week:int {0, 1, 2, 3, …}, default 0

A specific integer for the week of the month. e.g. 0 is 1st week of month, 1 is the 2nd week, etc.
weekday:int {0, 1, …, 6}, default 0

A specific integer for the day of the week.  0 is Monday 1 is Tuesday 2 is Wednesday 3 is Thursday 4 is Friday 5 is Saturday 6 is Sunday.      Attributes
base Returns a copy of the calling offset object with n=1 and all other attributes equal.
freqstr
kwds
n
name
nanos
normalize
rule_code
week
weekday     Methods
__call__(*args, **kwargs) Call self as a function.
rollback Roll provided date backward to next offset only if not on offset.
rollforward Roll provided date forward to next offset only if not on offset.
apply
apply_index
copy
isAnchored
is_anchored
is_month_end
is_month_start
is_on_offset
is_quarter_end
is_quarter_start
is_year_end
is_year_start
onOffset


pandas.tseries.offsets.WeekOfMonth.__call__   WeekOfMonth.__call__(*args, **kwargs)

Call self as a function.


pandas.tseries.offsets.WeekOfMonth.apply   WeekOfMonth.apply()


pandas.tseries.offsets.WeekOfMonth.apply_index   WeekOfMonth.apply_index(other)


pandas.tseries.offsets.WeekOfMonth.base   WeekOfMonth.base

Returns a copy of the calling offset object with n=1 and all other attributes equal.


pandas.tseries.offsets.WeekOfMonth.copy   WeekOfMonth.copy()


pandas.tseries.offsets.WeekOfMonth.freqstr   WeekOfMonth.freqstr


pandas.tseries.offsets.WeekOfMonth.is_anchored   WeekOfMonth.is_anchored()


pandas.tseries.offsets.WeekOfMonth.is_month_end   WeekOfMonth.is_month_end()


pandas.tseries.offsets.WeekOfMonth.is_month_start   WeekOfMonth.is_month_start()


pandas.tseries.offsets.WeekOfMonth.is_on_offset   WeekOfMonth.is_on_offset()


pandas.tseries.offsets.WeekOfMonth.is_quarter_end   WeekOfMonth.is_quarter_end()


pandas.tseries.offsets.WeekOfMonth.is_quarter_start   WeekOfMonth.is_quarter_start()


pandas.tseries.offsets.WeekOfMonth.is_year_end   WeekOfMonth.is_year_end()


pandas.tseries.offsets.WeekOfMonth.is_year_start   WeekOfMonth.is_year_start()


pandas.tseries.offsets.WeekOfMonth.isAnchored   WeekOfMonth.isAnchored()


pandas.tseries.offsets.WeekOfMonth.kwds   WeekOfMonth.kwds


pandas.tseries.offsets.WeekOfMonth.n   WeekOfMonth.n


pandas.tseries.offsets.WeekOfMonth.name   WeekOfMonth.name


pandas.tseries.offsets.WeekOfMonth.nanos   WeekOfMonth.nanos


pandas.tseries.offsets.WeekOfMonth.normalize   WeekOfMonth.normalize


pandas.tseries.offsets.WeekOfMonth.onOffset   WeekOfMonth.onOffset()


pandas.tseries.offsets.WeekOfMonth.rollback   WeekOfMonth.rollback()

Roll provided date backward to next offset only if not on offset.  Returns
 TimeStamp

Rolled timestamp if not on offset, otherwise unchanged timestamp.


pandas.tseries.offsets.WeekOfMonth.rollforward   WeekOfMonth.rollforward()

Roll provided date forward to next offset only if not on offset.  Returns
 TimeStamp

Rolled timestamp if not on offset, otherwise unchanged timestamp.


pandas.tseries.offsets.WeekOfMonth.rule_code   WeekOfMonth.rule_code


pandas.tseries.offsets.WeekOfMonth.week   WeekOfMonth.week


pandas.tseries.offsets.WeekOfMonth.weekday   WeekOfMonth.weekday


pandas.tseries.offsets.YearBegin   classpandas.tseries.offsets.YearBegin

DateOffset increments between calendar year begin dates. Attributes
base Returns a copy of the calling offset object with n=1 and all other attributes equal.
freqstr
kwds
month
n
name
nanos
normalize
rule_code     Methods
__call__(*args, **kwargs) Call self as a function.
rollback Roll provided date backward to next offset only if not on offset.
rollforward Roll provided date forward to next offset only if not on offset.
apply
apply_index
copy
isAnchored
is_anchored
is_month_end
is_month_start
is_on_offset
is_quarter_end
is_quarter_start
is_year_end
is_year_start
onOffset


pandas.tseries.offsets.YearBegin.__call__   YearBegin.__call__(*args, **kwargs)

Call self as a function.


pandas.tseries.offsets.YearBegin.apply   YearBegin.apply()


pandas.tseries.offsets.YearBegin.apply_index   YearBegin.apply_index(other)


pandas.tseries.offsets.YearBegin.base   YearBegin.base

Returns a copy of the calling offset object with n=1 and all other attributes equal.


pandas.tseries.offsets.YearBegin.copy   YearBegin.copy()


pandas.tseries.offsets.YearBegin.freqstr   YearBegin.freqstr


pandas.tseries.offsets.YearBegin.is_anchored   YearBegin.is_anchored()


pandas.tseries.offsets.YearBegin.is_month_end   YearBegin.is_month_end()


pandas.tseries.offsets.YearBegin.is_month_start   YearBegin.is_month_start()


pandas.tseries.offsets.YearBegin.is_on_offset   YearBegin.is_on_offset()


pandas.tseries.offsets.YearBegin.is_quarter_end   YearBegin.is_quarter_end()


pandas.tseries.offsets.YearBegin.is_quarter_start   YearBegin.is_quarter_start()


pandas.tseries.offsets.YearBegin.is_year_end   YearBegin.is_year_end()


pandas.tseries.offsets.YearBegin.is_year_start   YearBegin.is_year_start()


pandas.tseries.offsets.YearBegin.isAnchored   YearBegin.isAnchored()


pandas.tseries.offsets.YearBegin.kwds   YearBegin.kwds


pandas.tseries.offsets.YearBegin.month   YearBegin.month


pandas.tseries.offsets.YearBegin.n   YearBegin.n


pandas.tseries.offsets.YearBegin.name   YearBegin.name


pandas.tseries.offsets.YearBegin.nanos   YearBegin.nanos


pandas.tseries.offsets.YearBegin.normalize   YearBegin.normalize


pandas.tseries.offsets.YearBegin.onOffset   YearBegin.onOffset()


pandas.tseries.offsets.YearBegin.rollback   YearBegin.rollback()

Roll provided date backward to next offset only if not on offset.  Returns
 TimeStamp

Rolled timestamp if not on offset, otherwise unchanged timestamp.


pandas.tseries.offsets.YearBegin.rollforward   YearBegin.rollforward()

Roll provided date forward to next offset only if not on offset.  Returns
 TimeStamp

Rolled timestamp if not on offset, otherwise unchanged timestamp.


pandas.tseries.offsets.YearBegin.rule_code   YearBegin.rule_code


pandas.tseries.offsets.YearEnd   classpandas.tseries.offsets.YearEnd

DateOffset increments between calendar year ends. Attributes
base Returns a copy of the calling offset object with n=1 and all other attributes equal.
freqstr
kwds
month
n
name
nanos
normalize
rule_code     Methods
__call__(*args, **kwargs) Call self as a function.
rollback Roll provided date backward to next offset only if not on offset.
rollforward Roll provided date forward to next offset only if not on offset.
apply
apply_index
copy
isAnchored
is_anchored
is_month_end
is_month_start
is_on_offset
is_quarter_end
is_quarter_start
is_year_end
is_year_start
onOffset


pandas.tseries.offsets.YearEnd.__call__   YearEnd.__call__(*args, **kwargs)

Call self as a function.


pandas.tseries.offsets.YearEnd.apply   YearEnd.apply()


pandas.tseries.offsets.YearEnd.apply_index   YearEnd.apply_index(other)


pandas.tseries.offsets.YearEnd.base   YearEnd.base

Returns a copy of the calling offset object with n=1 and all other attributes equal.


pandas.tseries.offsets.YearEnd.copy   YearEnd.copy()


pandas.tseries.offsets.YearEnd.freqstr   YearEnd.freqstr


pandas.tseries.offsets.YearEnd.is_anchored   YearEnd.is_anchored()


pandas.tseries.offsets.YearEnd.is_month_end   YearEnd.is_month_end()


pandas.tseries.offsets.YearEnd.is_month_start   YearEnd.is_month_start()


pandas.tseries.offsets.YearEnd.is_on_offset   YearEnd.is_on_offset()


pandas.tseries.offsets.YearEnd.is_quarter_end   YearEnd.is_quarter_end()


pandas.tseries.offsets.YearEnd.is_quarter_start   YearEnd.is_quarter_start()


pandas.tseries.offsets.YearEnd.is_year_end   YearEnd.is_year_end()


pandas.tseries.offsets.YearEnd.is_year_start   YearEnd.is_year_start()


pandas.tseries.offsets.YearEnd.isAnchored   YearEnd.isAnchored()


pandas.tseries.offsets.YearEnd.kwds   YearEnd.kwds


pandas.tseries.offsets.YearEnd.month   YearEnd.month


pandas.tseries.offsets.YearEnd.n   YearEnd.n


pandas.tseries.offsets.YearEnd.name   YearEnd.name


pandas.tseries.offsets.YearEnd.nanos   YearEnd.nanos


pandas.tseries.offsets.YearEnd.normalize   YearEnd.normalize


pandas.tseries.offsets.YearEnd.onOffset   YearEnd.onOffset()


pandas.tseries.offsets.YearEnd.rollback   YearEnd.rollback()

Roll provided date backward to next offset only if not on offset.  Returns
 TimeStamp

Rolled timestamp if not on offset, otherwise unchanged timestamp.


pandas.tseries.offsets.YearEnd.rollforward   YearEnd.rollforward()

Roll provided date forward to next offset only if not on offset.  Returns
 TimeStamp

Rolled timestamp if not on offset, otherwise unchanged timestamp.


pandas.tseries.offsets.YearEnd.rule_code   YearEnd.rule_code


pandas.UInt16Dtype   classpandas.UInt16Dtype[source]

An ExtensionDtype for uint16 integer data.  Changed in version 1.0.0: Now uses pandas.NA as its missing value, rather than numpy.nan.  Attributes
None     Methods
None


pandas.UInt32Dtype   classpandas.UInt32Dtype[source]

An ExtensionDtype for uint32 integer data.  Changed in version 1.0.0: Now uses pandas.NA as its missing value, rather than numpy.nan.  Attributes
None     Methods
None


pandas.UInt64Dtype   classpandas.UInt64Dtype[source]

An ExtensionDtype for uint64 integer data.  Changed in version 1.0.0: Now uses pandas.NA as its missing value, rather than numpy.nan.  Attributes
None     Methods
None


pandas.UInt64Index   classpandas.UInt64Index(data=None, dtype=None, copy=False, name=None)[source]

Immutable sequence used for indexing and alignment. The basic object storing axis labels for all pandas objects. UInt64Index is a special case of Index with purely unsigned integer labels. .  Deprecated since version 1.4.0: In pandas v2.0 UInt64Index will be removed and NumericIndex used instead. UInt64Index will remain fully functional for the duration of pandas 1.x.   Parameters

data:array-like (1-dimensional)

dtype:NumPy dtype (default: uint64)

copy:bool

Make a copy of input ndarray.
name:object

Name to be stored in the index.      See also  Index

The base pandas Index type.  NumericIndex

Index of numpy int/uint/float data.    Notes An Index instance can only contain hashable objects. Attributes
None     Methods
None


pandas.UInt8Dtype   classpandas.UInt8Dtype[source]

An ExtensionDtype for uint8 integer data.  Changed in version 1.0.0: Now uses pandas.NA as its missing value, rather than numpy.nan.  Attributes
None     Methods
None


pandas.unique   pandas.unique(values)[source]

Return unique values based on a hash table. Uniques are returned in order of appearance. This does NOT sort. Significantly faster than numpy.unique for long enough sequences. Includes NA values.  Parameters

values:1d array-like

  Returns
 numpy.ndarray or ExtensionArray

The return can be:  Index : when the input is an Index Categorical : when the input is a Categorical dtype ndarray : when the input is a Series/ndarray  Return numpy.ndarray or ExtensionArray.      See also  Index.unique

Return unique values from an Index.  Series.unique

Return unique values of Series object.    Examples
>>> pd.unique(pd.Series([2, 1, 3, 3]))
array([2, 1, 3])

>>> pd.unique(pd.Series([2] + [1] * 5))
array([2, 1])

>>> pd.unique(pd.Series([pd.Timestamp("20160101"), pd.Timestamp("20160101")]))
array(['2016-01-01T00:00:00.000000000'], dtype='datetime64[ns]')

>>> pd.unique(
...     pd.Series(
...         [
...             pd.Timestamp("20160101", tz="US/Eastern"),
...             pd.Timestamp("20160101", tz="US/Eastern"),
...         ]
...     )
... )
<DatetimeArray>
['2016-01-01 00:00:00-05:00']
Length: 1, dtype: datetime64[ns, US/Eastern]

>>> pd.unique(
...     pd.Index(
...         [
...             pd.Timestamp("20160101", tz="US/Eastern"),
...             pd.Timestamp("20160101", tz="US/Eastern"),
...         ]
...     )
... )
DatetimeIndex(['2016-01-01 00:00:00-05:00'],
        dtype='datetime64[ns, US/Eastern]',
        freq=None)

>>> pd.unique(list("baabc"))
array(['b', 'a', 'c'], dtype=object)
  An unordered Categorical will return categories in the order of appearance.
>>> pd.unique(pd.Series(pd.Categorical(list("baabc"))))
['b', 'a', 'c']
Categories (3, object): ['a', 'b', 'c']

>>> pd.unique(pd.Series(pd.Categorical(list("baabc"), categories=list("abc"))))
['b', 'a', 'c']
Categories (3, object): ['a', 'b', 'c']
  An ordered Categorical preserves the category ordering.
>>> pd.unique(
...     pd.Series(
...         pd.Categorical(list("baabc"), categories=list("abc"), ordered=True)
...     )
... )
['b', 'a', 'c']
Categories (3, object): ['a' < 'b' < 'c']
  An array of tuples
>>> pd.unique([("a", "b"), ("b", "a"), ("a", "c"), ("b", "a")])
array([('a', 'b'), ('b', 'a'), ('a', 'c')], dtype=object)


pandas.util.hash_array   pandas.util.hash_array(vals, encoding='utf8', hash_key='0123456789123456', categorize=True)[source]

Given a 1d array, return an array of deterministic integers.  Parameters

vals:ndarray or ExtensionArray

encoding:str, default ‘utf8’

Encoding for data & key when strings.
hash_key:str, default _default_hash_key

Hash_key for string key to encode.
categorize:bool, default True

Whether to first categorize object arrays before hashing. This is more efficient when the array contains duplicate values.    Returns
 ndarray[np.uint64, ndim=1]

Hashed values, same length as the vals.


pandas.util.hash_pandas_object   pandas.util.hash_pandas_object(obj, index=True, encoding='utf8', hash_key='0123456789123456', categorize=True)[source]

Return a data hash of the Index/Series/DataFrame.  Parameters

obj:Index, Series, or DataFrame

index:bool, default True

Include the index in the hash (if Series/DataFrame).
encoding:str, default ‘utf8’

Encoding for data & key when strings.
hash_key:str, default _default_hash_key

Hash_key for string key to encode.
categorize:bool, default True

Whether to first categorize object arrays before hashing. This is more efficient when the array contains duplicate values.    Returns
 Series of uint64, same length as the object


pandas.wide_to_long   pandas.wide_to_long(df, stubnames, i, j, sep='', suffix='\\d+')[source]

Unpivot a DataFrame from wide to long format. Less flexible but more user-friendly than melt. With stubnames [‘A’, ‘B’], this function expects to find one or more group of columns with format A-suffix1, A-suffix2,…, B-suffix1, B-suffix2,… You specify what you want to call this suffix in the resulting long format with j (for example j=’year’) Each row of these wide variables are assumed to be uniquely identified by i (can be a single column name or a list of column names) All remaining variables in the data frame are left intact.  Parameters

df:DataFrame

The wide-format DataFrame.
stubnames:str or list-like

The stub name(s). The wide format variables are assumed to start with the stub names.
i:str or list-like

Column(s) to use as id variable(s).
j:str

The name of the sub-observation variable. What you wish to name your suffix in the long format.
sep:str, default “”

A character indicating the separation of the variable names in the wide format, to be stripped from the names in the long format. For example, if your column names are A-suffix1, A-suffix2, you can strip the hyphen by specifying sep=’-’.
suffix:str, default ‘\d+’

A regular expression capturing the wanted suffixes. ‘\d+’ captures numeric suffixes. Suffixes with no numbers could be specified with the negated character class ‘\D+’. You can also further disambiguate suffixes, for example, if your wide variables are of the form A-one, B-two,.., and you have an unrelated column A-rating, you can ignore the last one by specifying suffix=’(!?one|two)’. When all suffixes are numeric, they are cast to int64/float64.    Returns
 DataFrame

A DataFrame that contains each stub name as a variable, with new index (i, j).      See also  melt

Unpivot a DataFrame from wide to long format, optionally leaving identifiers set.  pivot

Create a spreadsheet-style pivot table as a DataFrame.  DataFrame.pivot

Pivot without aggregation that can handle non-numeric data.  DataFrame.pivot_table

Generalization of pivot that can handle duplicate values for one index/column pair.  DataFrame.unstack

Pivot based on the index values instead of a column.    Notes All extra variables are left untouched. This simply uses pandas.melt under the hood, but is hard-coded to “do the right thing” in a typical case. Examples
>>> np.random.seed(123)
>>> df = pd.DataFrame({"A1970" : {0 : "a", 1 : "b", 2 : "c"},
...                    "A1980" : {0 : "d", 1 : "e", 2 : "f"},
...                    "B1970" : {0 : 2.5, 1 : 1.2, 2 : .7},
...                    "B1980" : {0 : 3.2, 1 : 1.3, 2 : .1},
...                    "X"     : dict(zip(range(3), np.random.randn(3)))
...                   })
>>> df["id"] = df.index
>>> df
  A1970 A1980  B1970  B1980         X  id
0     a     d    2.5    3.2 -1.085631   0
1     b     e    1.2    1.3  0.997345   1
2     c     f    0.7    0.1  0.282978   2
>>> pd.wide_to_long(df, ["A", "B"], i="id", j="year")
...
                X  A    B
id year
0  1970 -1.085631  a  2.5
1  1970  0.997345  b  1.2
2  1970  0.282978  c  0.7
0  1980 -1.085631  d  3.2
1  1980  0.997345  e  1.3
2  1980  0.282978  f  0.1
  With multiple id columns
>>> df = pd.DataFrame({
...     'famid': [1, 1, 1, 2, 2, 2, 3, 3, 3],
...     'birth': [1, 2, 3, 1, 2, 3, 1, 2, 3],
...     'ht1': [2.8, 2.9, 2.2, 2, 1.8, 1.9, 2.2, 2.3, 2.1],
...     'ht2': [3.4, 3.8, 2.9, 3.2, 2.8, 2.4, 3.3, 3.4, 2.9]
... })
>>> df
   famid  birth  ht1  ht2
0      1      1  2.8  3.4
1      1      2  2.9  3.8
2      1      3  2.2  2.9
3      2      1  2.0  3.2
4      2      2  1.8  2.8
5      2      3  1.9  2.4
6      3      1  2.2  3.3
7      3      2  2.3  3.4
8      3      3  2.1  2.9
>>> l = pd.wide_to_long(df, stubnames='ht', i=['famid', 'birth'], j='age')
>>> l
...
                  ht
famid birth age
1     1     1    2.8
            2    3.4
      2     1    2.9
            2    3.8
      3     1    2.2
            2    2.9
2     1     1    2.0
            2    3.2
      2     1    1.8
            2    2.8
      3     1    1.9
            2    2.4
3     1     1    2.2
            2    3.3
      2     1    2.3
            2    3.4
      3     1    2.1
            2    2.9
  Going from long back to wide just takes some creative use of unstack
>>> w = l.unstack()
>>> w.columns = w.columns.map('{0[0]}{0[1]}'.format)
>>> w.reset_index()
   famid  birth  ht1  ht2
0      1      1  2.8  3.4
1      1      2  2.9  3.8
2      1      3  2.2  2.9
3      2      1  2.0  3.2
4      2      2  1.8  2.8
5      2      3  1.9  2.4
6      3      1  2.2  3.3
7      3      2  2.3  3.4
8      3      3  2.1  2.9
  Less wieldy column names are also handled
>>> np.random.seed(0)
>>> df = pd.DataFrame({'A(weekly)-2010': np.random.rand(3),
...                    'A(weekly)-2011': np.random.rand(3),
...                    'B(weekly)-2010': np.random.rand(3),
...                    'B(weekly)-2011': np.random.rand(3),
...                    'X' : np.random.randint(3, size=3)})
>>> df['id'] = df.index
>>> df
   A(weekly)-2010  A(weekly)-2011  B(weekly)-2010  B(weekly)-2011  X  id
0        0.548814        0.544883        0.437587        0.383442  0   0
1        0.715189        0.423655        0.891773        0.791725  1   1
2        0.602763        0.645894        0.963663        0.528895  1   2

>>> pd.wide_to_long(df, ['A(weekly)', 'B(weekly)'], i='id',
...                 j='year', sep='-')
...
         X  A(weekly)  B(weekly)
id year
0  2010  0   0.548814   0.437587
1  2010  1   0.715189   0.891773
2  2010  1   0.602763   0.963663
0  2011  0   0.544883   0.383442
1  2011  1   0.423655   0.791725
2  2011  1   0.645894   0.528895
  If we have many columns, we could also use a regex to find our stubnames and pass that list on to wide_to_long
>>> stubnames = sorted(
...     set([match[0] for match in df.columns.str.findall(
...         r'[A-B]\(.*\)').values if match != []])
... )
>>> list(stubnames)
['A(weekly)', 'B(weekly)']
  All of the above examples have integers as suffixes. It is possible to have non-integers as suffixes.
>>> df = pd.DataFrame({
...     'famid': [1, 1, 1, 2, 2, 2, 3, 3, 3],
...     'birth': [1, 2, 3, 1, 2, 3, 1, 2, 3],
...     'ht_one': [2.8, 2.9, 2.2, 2, 1.8, 1.9, 2.2, 2.3, 2.1],
...     'ht_two': [3.4, 3.8, 2.9, 3.2, 2.8, 2.4, 3.3, 3.4, 2.9]
... })
>>> df
   famid  birth  ht_one  ht_two
0      1      1     2.8     3.4
1      1      2     2.9     3.8
2      1      3     2.2     2.9
3      2      1     2.0     3.2
4      2      2     1.8     2.8
5      2      3     1.9     2.4
6      3      1     2.2     3.3
7      3      2     2.3     3.4
8      3      3     2.1     2.9

>>> l = pd.wide_to_long(df, stubnames='ht', i=['famid', 'birth'], j='age',
...                     sep='_', suffix=r'\w+')
>>> l
...
                  ht
famid birth age
1     1     one  2.8
            two  3.4
      2     one  2.9
            two  3.8
      3     one  2.2
            two  2.9
2     1     one  2.0
            two  3.2
      2     one  1.8
            two  2.8
      3     one  1.9
            two  2.4
3     1     one  2.2
            two  3.3
      2     one  2.3
            two  3.4
      3     one  2.1
            two  2.9


Plotting The following functions are contained in the pandas.plotting module.
andrews_curves(frame, class_column[, ax, ...]) Generate a matplotlib plot of Andrews curves, for visualising clusters of multivariate data.
autocorrelation_plot(series[, ax]) Autocorrelation plot for time series.
bootstrap_plot(series[, fig, size, samples]) Bootstrap plot on mean, median and mid-range statistics.
boxplot(data[, column, by, ax, fontsize, ...]) Make a box plot from DataFrame columns.
deregister_matplotlib_converters() Remove pandas formatters and converters.
lag_plot(series[, lag, ax]) Lag plot for time series.
parallel_coordinates(frame, class_column[, ...]) Parallel coordinates plotting.
plot_params Stores pandas plotting options.
radviz(frame, class_column[, ax, color, ...]) Plot a multidimensional dataset in 2D.
register_matplotlib_converters() Register pandas formatters and converters with matplotlib.
scatter_matrix(frame[, alpha, figsize, ax, ...]) Draw a matrix of scatter plots.
table(ax, data[, rowLabels, colLabels]) Helper function to convert DataFrame and Series to matplotlib.table.


Resampling Resampler objects are returned by resample calls: pandas.DataFrame.resample(), pandas.Series.resample().  Indexing, iteration
Resampler.__iter__() Groupby iterator.
Resampler.groups Dict {group name -> group labels}.
Resampler.indices Dict {group name -> group indices}.
Resampler.get_group(name[, obj]) Construct DataFrame from group with provided name.      Function application
Resampler.apply([func]) Aggregate using one or more operations over the specified axis.
Resampler.aggregate([func]) Aggregate using one or more operations over the specified axis.
Resampler.transform(arg, *args, **kwargs) Call function producing a like-indexed Series on each group and return a Series with the transformed values.
Resampler.pipe(func, *args, **kwargs) Apply a function func with arguments to this Resampler object and return the function's result.      Upsampling
Resampler.ffill([limit]) Forward fill the values.
Resampler.backfill([limit]) Backward fill the new missing values in the resampled data.
Resampler.bfill([limit]) Backward fill the new missing values in the resampled data.
Resampler.pad([limit]) Forward fill the values.
Resampler.nearest([limit]) Resample by using the nearest value.
Resampler.fillna(method[, limit]) Fill missing values introduced by upsampling.
Resampler.asfreq([fill_value]) Return the values at the new freq, essentially a reindex.
Resampler.interpolate([method, axis, limit, ...]) Interpolate values according to different methods.      Computations / descriptive stats
Resampler.count() Compute count of group, excluding missing values.
Resampler.nunique([_method]) Return number of unique elements in the group.
Resampler.first([_method, min_count]) Compute first of group values.
Resampler.last([_method, min_count]) Compute last of group values.
Resampler.max([_method, min_count]) Compute max of group values.
Resampler.mean([_method]) Compute mean of groups, excluding missing values.
Resampler.median([_method]) Compute median of groups, excluding missing values.
Resampler.min([_method, min_count]) Compute min of group values.
Resampler.ohlc([_method]) Compute open, high, low and close values of a group, excluding missing values.
Resampler.prod([_method, min_count]) Compute prod of group values.
Resampler.size() Compute group sizes.
Resampler.sem([_method]) Compute standard error of the mean of groups, excluding missing values.
Resampler.std([ddof]) Compute standard deviation of groups, excluding missing values.
Resampler.sum([_method, min_count]) Compute sum of group values.
Resampler.var([ddof]) Compute variance of groups, excluding missing values.
Resampler.quantile([q]) Return value at the given quantile.