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 __new__(cls, data=None, ordinal=None, freq=None, start=None, end=None, periods=None, copy=False, name=None, tz=None, dtype=None, **kwargs): if periods is not None: if is_float(periods): periods = int(periods) elif not is_integer(periods): msg = 'periods must be a number, got {periods}' raise TypeError(msg.format(periods=periods)) if name is None and hasattr(data, 'name'): name = data.name if dtype is not None: dtype = pandas_dtype(dtype) if not is_period_dtype(dtype): raise ValueError('dtype must be PeriodDtype') if freq is None: freq = dtype.freq elif freq != dtype.freq: msg = 'specified freq and dtype are different' raise IncompatibleFrequency(msg) # coerce freq to freq object, otherwise it can be coerced elementwise # which is slow if freq: freq = Period._maybe_convert_freq(freq) if data is None: if ordinal is not None: data = np.asarray(ordinal, dtype=np.int64) else: data, freq = cls._generate_range(start, end, periods, freq, kwargs) return cls._from_ordinals(data, name=name, freq=freq) if isinstance(data, PeriodIndex): if freq is None or freq == data.freq: # no freq change freq = data.freq data = data._values else: base1, _ = _gfc(data.freq) base2, _ = _gfc(freq) data = period.period_asfreq_arr(data._values, base1, base2, 1) return cls._simple_new(data, name=name, freq=freq) # not array / index if not isinstance( data, (np.ndarray, PeriodIndex, DatetimeIndex, Int64Index)): if is_scalar(data) or isinstance(data, Period): cls._scalar_data_error(data) # other iterable of some kind if not isinstance(data, (list, tuple)): data = list(data) data = np.asarray(data) # datetime other than period if is_datetime64_dtype(data.dtype): data = dt64arr_to_periodarr(data, freq, tz) return cls._from_ordinals(data, name=name, freq=freq) # check not floats if infer_dtype(data) == 'floating' and len(data) > 0: raise TypeError("PeriodIndex does not allow " "floating point in construction") # anything else, likely an array of strings or periods data = _ensure_object(data) freq = freq or period.extract_freq(data) data = period.extract_ordinals(data, freq) return cls._from_ordinals(data, name=name, freq=freq)
def __new__(cls, data=None, ordinal=None, freq=None, start=None, end=None, periods=None, copy=False, name=None, tz=None, dtype=None, **kwargs): if periods is not None: if is_float(periods): periods = int(periods) elif not is_integer(periods): raise ValueError('Periods must be a number, got %s' % str(periods)) if name is None and hasattr(data, 'name'): name = data.name if dtype is not None: dtype = pandas_dtype(dtype) if not is_period_dtype(dtype): raise ValueError('dtype must be PeriodDtype') if freq is None: freq = dtype.freq elif freq != dtype.freq: msg = 'specified freq and dtype are different' raise IncompatibleFrequency(msg) # coerce freq to freq object, otherwise it can be coerced elementwise # which is slow if freq: freq = Period._maybe_convert_freq(freq) if data is None: if ordinal is not None: data = np.asarray(ordinal, dtype=np.int64) else: data, freq = cls._generate_range(start, end, periods, freq, kwargs) return cls._from_ordinals(data, name=name, freq=freq) if isinstance(data, PeriodIndex): if freq is None or freq == data.freq: # no freq change freq = data.freq data = data._values else: base1, _ = _gfc(data.freq) base2, _ = _gfc(freq) data = period.period_asfreq_arr(data._values, base1, base2, 1) return cls._simple_new(data, name=name, freq=freq) # not array / index if not isinstance(data, (np.ndarray, PeriodIndex, DatetimeIndex, Int64Index)): if is_scalar(data) or isinstance(data, Period): cls._scalar_data_error(data) # other iterable of some kind if not isinstance(data, (list, tuple)): data = list(data) data = np.asarray(data) # datetime other than period if is_datetime64_dtype(data.dtype): data = dt64arr_to_periodarr(data, freq, tz) return cls._from_ordinals(data, name=name, freq=freq) # check not floats if infer_dtype(data) == 'floating' and len(data) > 0: raise TypeError("PeriodIndex does not allow " "floating point in construction") # anything else, likely an array of strings or periods data = _ensure_object(data) freq = freq or period.extract_freq(data) data = period.extract_ordinals(data, freq) return cls._from_ordinals(data, name=name, freq=freq)