def asfreq(self, freq=None, how='E'):
"""
Convert the PeriodIndex to the specified frequency `freq`.
Parameters
----------
freq : str
a frequency
how : str {'E', 'S'}
'E', 'END', or 'FINISH' for end,
'S', 'START', or 'BEGIN' for start.
Whether the elements should be aligned to the end
or start within pa period. January 31st ('END') vs.
Janury 1st ('START') for example.
Returns
-------
new : PeriodIndex with the new frequency
Examples
--------
>>> pidx = pd.period_range('2010-01-01', '2015-01-01', freq='A')
>>> pidx
<class 'pandas.core.indexes.period.PeriodIndex'>
[2010, ..., 2015]
Length: 6, Freq: A-DEC
>>> pidx.asfreq('M')
<class 'pandas.core.indexes.period.PeriodIndex'>
[2010-12, ..., 2015-12]
Length: 6, Freq: M
>>> pidx.asfreq('M', how='S')
<class 'pandas.core.indexes.period.PeriodIndex'>
[2010-01, ..., 2015-01]
Length: 6, Freq: M
"""
how = _validate_end_alias(how)
freq = Period._maybe_convert_freq(freq)
base1, mult1 = _gfc(self.freq)
base2, mult2 = _gfc(freq)
asi8 = self.asi8
# mult1 can't be negative or 0
end = how == 'E'
if end:
ordinal = asi8 + mult1 - 1
else:
ordinal = asi8
new_data = period.period_asfreq_arr(ordinal, base1, base2, end)
if self.hasnans:
new_data[self._isnan] = tslib.iNaT
return self._simple_new(new_data, self.name, freq=freq)
def to_timestamp(self, freq=None, how='start'):
"""
Cast to DatetimeIndex
Parameters
----------
freq : string or DateOffset, optional
Target frequency. The default is 'D' for week or longer,
'S' otherwise
how : {'s', 'e', 'start', 'end'}
Returns
-------
DatetimeIndex
"""
how = _validate_end_alias(how)
if freq is None:
base, mult = _gfc(self.freq)
freq = frequencies.get_to_timestamp_base(base)
else:
freq = Period._maybe_convert_freq(freq)
base, mult = _gfc(freq)
new_data = self.asfreq(freq, how)
new_data = period.periodarr_to_dt64arr(new_data._values, base)
return DatetimeIndex(new_data, freq='infer', name=self.name)
def to_timestamp(self, freq=None, how='start'):
"""
Cast to DatetimeIndex
Parameters
----------
freq : string or DateOffset, optional
Target frequency. The default is 'D' for week or longer,
'S' otherwise
how : {'s', 'e', 'start', 'end'}
Returns
-------
DatetimeIndex
"""
how = _validate_end_alias(how)
if freq is None:
base, mult = _gfc(self.freq)
freq = frequencies.get_to_timestamp_base(base)
else:
freq = Period._maybe_convert_freq(freq)
base, mult = _gfc(freq)
new_data = self.asfreq(freq, how)
new_data = period.periodarr_to_dt64arr(new_data._values, base)
return DatetimeIndex(new_data, freq='infer', name=self.name)
def asfreq(self, freq=None, how='E'):
"""
Convert the PeriodIndex to the specified frequency `freq`.
Parameters
----------
freq : str
a frequency
how : str {'E', 'S'}
'E', 'END', or 'FINISH' for end,
'S', 'START', or 'BEGIN' for start.
Whether the elements should be aligned to the end
or start within pa period. January 31st ('END') vs.
Janury 1st ('START') for example.
Returns
-------
new : PeriodIndex with the new frequency
Examples
--------
>>> pidx = pd.period_range('2010-01-01', '2015-01-01', freq='A')
>>> pidx
<class 'pandas.core.indexes.period.PeriodIndex'>
[2010, ..., 2015]
Length: 6, Freq: A-DEC
>>> pidx.asfreq('M')
<class 'pandas.core.indexes.period.PeriodIndex'>
[2010-12, ..., 2015-12]
Length: 6, Freq: M
>>> pidx.asfreq('M', how='S')
<class 'pandas.core.indexes.period.PeriodIndex'>
[2010-01, ..., 2015-01]
Length: 6, Freq: M
"""
how = _validate_end_alias(how)
freq = Period._maybe_convert_freq(freq)
base1, mult1 = _gfc(self.freq)
base2, mult2 = _gfc(freq)
asi8 = self.asi8
# mult1 can't be negative or 0
end = how == 'E'
if end:
ordinal = asi8 + mult1 - 1
else:
ordinal = asi8
new_data = period.period_asfreq_arr(ordinal, base1, base2, end)
if self.hasnans:
new_data[self._isnan] = tslib.iNaT
return self._simple_new(new_data, self.name, freq=freq)
def asfreq(self, freq=None, how="E") -> "PeriodArray":
"""
Convert the Period Array/Index to the specified frequency `freq`.
Parameters
----------
freq : str
A frequency.
how : str {'E', 'S'}
Whether the elements should be aligned to the end
or start within pa period.
* 'E', 'END', or 'FINISH' for end,
* 'S', 'START', or 'BEGIN' for start.
January 31st ('END') vs. January 1st ('START') for example.
Returns
-------
Period Array/Index
Constructed with the new frequency.
Examples
--------
>>> pidx = pd.period_range('2010-01-01', '2015-01-01', freq='A')
>>> pidx
PeriodIndex(['2010', '2011', '2012', '2013', '2014', '2015'],
dtype='period[A-DEC]', freq='A-DEC')
>>> pidx.asfreq('M')
PeriodIndex(['2010-12', '2011-12', '2012-12', '2013-12', '2014-12',
'2015-12'], dtype='period[M]', freq='M')
>>> pidx.asfreq('M', how='S')
PeriodIndex(['2010-01', '2011-01', '2012-01', '2013-01', '2014-01',
'2015-01'], dtype='period[M]', freq='M')
"""
how = libperiod._validate_end_alias(how)
freq = Period._maybe_convert_freq(freq)
base1, mult1 = libfrequencies.get_freq_code(self.freq)
base2, mult2 = libfrequencies.get_freq_code(freq)
asi8 = self.asi8
# mult1 can't be negative or 0
end = how == "E"
if end:
ordinal = asi8 + mult1 - 1
else:
ordinal = asi8
new_data = period_asfreq_arr(ordinal, base1, base2, end)
if self._hasnans:
new_data[self._isnan] = iNaT
return type(self)(new_data, freq=freq)
def asfreq(self, freq=None, how='E'):
"""
Convert the Period Array/Index to the specified frequency `freq`.
Parameters
----------
freq : str
a frequency
how : str {'E', 'S'}
'E', 'END', or 'FINISH' for end,
'S', 'START', or 'BEGIN' for start.
Whether the elements should be aligned to the end
or start within pa period. January 31st ('END') vs.
January 1st ('START') for example.
Returns
-------
new : Period Array/Index with the new frequency
Examples
--------
>>> pidx = pd.period_range('2010-01-01', '2015-01-01', freq='A')
>>> pidx
PeriodIndex(['2010', '2011', '2012', '2013', '2014', '2015'],
dtype='period[A-DEC]', freq='A-DEC')
>>> pidx.asfreq('M')
PeriodIndex(['2010-12', '2011-12', '2012-12', '2013-12', '2014-12',
'2015-12'], dtype='period[M]', freq='M')
>>> pidx.asfreq('M', how='S')
PeriodIndex(['2010-01', '2011-01', '2012-01', '2013-01', '2014-01',
'2015-01'], dtype='period[M]', freq='M')
"""
how = libperiod._validate_end_alias(how)
freq = Period._maybe_convert_freq(freq)
base1, mult1 = libfrequencies.get_freq_code(self.freq)
base2, mult2 = libfrequencies.get_freq_code(freq)
asi8 = self.asi8
# mult1 can't be negative or 0
end = how == 'E'
if end:
ordinal = asi8 + mult1 - 1
else:
ordinal = asi8
new_data = period_asfreq_arr(ordinal, base1, base2, end)
if self._hasnans:
new_data[self._isnan] = iNaT
return type(self)(new_data, freq=freq)
def to_timestamp(self, freq=None, how="start"):
"""
Cast to DatetimeArray/Index.
Parameters
----------
freq : str or DateOffset, optional
Target frequency. The default is 'D' for week or longer,
'S' otherwise.
how : {'s', 'e', 'start', 'end'}
Whether to use the start or end of the time period being converted.
Returns
-------
DatetimeArray/Index
"""
from pandas.core.arrays import DatetimeArray
how = libperiod._validate_end_alias(how)
end = how == "E"
if end:
if freq == "B":
# roll forward to ensure we land on B date
adjust = Timedelta(1, "D") - Timedelta(1, "ns")
return self.to_timestamp(how="start") + adjust
else:
adjust = Timedelta(1, "ns")
return (self + self.freq).to_timestamp(how="start") - adjust
if freq is None:
base, mult = libfrequencies.get_freq_code(self.freq)
freq = libfrequencies.get_to_timestamp_base(base)
else:
freq = Period._maybe_convert_freq(freq)
base, mult = libfrequencies.get_freq_code(freq)
new_data = self.asfreq(freq, how=how)
new_data = libperiod.periodarr_to_dt64arr(new_data.asi8, base)
return DatetimeArray(new_data)._with_freq("infer")
def to_timestamp(self, freq=None, how='start'):
"""
Cast to DatetimeArray/Index
Parameters
----------
freq : string or DateOffset, optional
Target frequency. The default is 'D' for week or longer,
'S' otherwise
how : {'s', 'e', 'start', 'end'}
Returns
-------
DatetimeArray/Index
"""
from pandas.core.arrays import DatetimeArrayMixin
how = libperiod._validate_end_alias(how)
end = how == 'E'
if end:
if freq == 'B':
# roll forward to ensure we land on B date
adjust = Timedelta(1, 'D') - Timedelta(1, 'ns')
return self.to_timestamp(how='start') + adjust
else:
adjust = Timedelta(1, 'ns')
return (self + 1).to_timestamp(how='start') - adjust
if freq is None:
base, mult = frequencies.get_freq_code(self.freq)
freq = frequencies.get_to_timestamp_base(base)
else:
freq = Period._maybe_convert_freq(freq)
base, mult = frequencies.get_freq_code(freq)
new_data = self.asfreq(freq, how=how)
new_data = libperiod.periodarr_to_dt64arr(new_data._ndarray_values,
base)
return DatetimeArrayMixin(new_data, freq='infer')
def to_timestamp(self, freq=None, how='start'):
"""
Cast to DatetimeArray/Index
Parameters
----------
freq : string or DateOffset, optional
Target frequency. The default is 'D' for week or longer,
'S' otherwise
how : {'s', 'e', 'start', 'end'}
Returns
-------
DatetimeArray/Index
"""
from pandas.core.arrays import DatetimeArrayMixin
how = libperiod._validate_end_alias(how)
end = how == 'E'
if end:
if freq == 'B':
# roll forward to ensure we land on B date
adjust = Timedelta(1, 'D') - Timedelta(1, 'ns')
return self.to_timestamp(how='start') + adjust
else:
adjust = Timedelta(1, 'ns')
return (self + 1).to_timestamp(how='start') - adjust
if freq is None:
base, mult = frequencies.get_freq_code(self.freq)
freq = frequencies.get_to_timestamp_base(base)
else:
freq = Period._maybe_convert_freq(freq)
base, mult = frequencies.get_freq_code(freq)
new_data = self.asfreq(freq, how=how)
new_data = libperiod.periodarr_to_dt64arr(new_data._ndarray_values,
base)
return DatetimeArrayMixin(new_data, freq='infer')
