def _from_arraylike(cls, data, freq, tz): if freq is not None: freq = Period._maybe_convert_freq(freq) if not isinstance(data, (np.ndarray, PeriodIndex, DatetimeIndex, Int64Index)): if is_scalar(data) or isinstance(data, Period): raise ValueError('PeriodIndex() must be called with a ' 'collection of some kind, %s was passed' % repr(data)) # other iterable of some kind if not isinstance(data, (list, tuple)): data = list(data) try: data = _ensure_int64(data) if freq is None: raise ValueError('freq not specified') data = np.array([Period(x, freq=freq) for x in data], dtype=np.int64) except (TypeError, ValueError): data = _ensure_object(data) if freq is None: freq = period.extract_freq(data) data = period.extract_ordinals(data, freq) else: if isinstance(data, PeriodIndex): if freq is None or freq == data.freq: freq = data.freq data = data._values else: base1, _ = _gfc(data.freq) base2, _ = _gfc(freq) data = period.period_asfreq_arr(data._values, base1, base2, 1) else: if is_object_dtype(data): inferred = infer_dtype(data) if inferred == 'integer': data = data.astype(np.int64) if freq is None and is_object_dtype(data): # must contain Period instance and thus extract ordinals freq = period.extract_freq(data) data = period.extract_ordinals(data, freq) if freq is None: msg = 'freq not specified and cannot be inferred' raise ValueError(msg) if data.dtype != np.int64: if np.issubdtype(data.dtype, np.datetime64): data = dt64arr_to_periodarr(data, freq, tz) else: data = _ensure_object(data) data = period.extract_ordinals(data, freq) return data, freq
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.tseries.period.PeriodIndex'> [2010, ..., 2015] Length: 6, Freq: A-DEC >>> pidx.asfreq('M') <class 'pandas.tseries.period.PeriodIndex'> [2010-12, ..., 2015-12] Length: 6, Freq: M >>> pidx.asfreq('M', how='S') <class 'pandas.tseries.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'): how = _validate_end_alias(how) freq = _freq_mod.get_standard_freq(freq) base1, mult1 = _gfc(self.freq) if isinstance(freq, basestring): base2, mult2 = _gfc(freq) else: base2, mult2 = freq if mult2 != 1: raise ValueError('Only mult == 1 supported') if how not in ('S', 'E'): raise ValueError('relation argument must be one of S or E') end = how == 'E' new_data = plib.period_asfreq_arr(self.values, base1, base2, end) result = new_data.view(PeriodIndex) result.name = self.name result.freq = freq return result
def _from_arraylike(cls, data, freq, tz): if not isinstance(data, (np.ndarray, PeriodIndex, DatetimeIndex, Int64Index)): if np.isscalar(data) or isinstance(data, Period): raise ValueError('PeriodIndex() must be called with a ' 'collection of some kind, %s was passed' % repr(data)) # other iterable of some kind if not isinstance(data, (list, tuple)): data = list(data) try: data = com._ensure_int64(data) if freq is None: raise ValueError('freq not specified') data = np.array([Period(x, freq=freq).ordinal for x in data], dtype=np.int64) except (TypeError, ValueError): data = com._ensure_object(data) if freq is None and len(data) > 0: freq = getattr(data[0], 'freq', None) if freq is None: raise ValueError('freq not specified and cannot be ' 'inferred from first element') data = _get_ordinals(data, freq) else: if isinstance(data, PeriodIndex): if freq is None or freq == data.freq: freq = data.freq data = data.values else: base1, _ = _gfc(data.freq) base2, _ = _gfc(freq) data = period.period_asfreq_arr(data.values, base1, base2, 1) else: if freq is None and len(data) > 0: freq = getattr(data[0], 'freq', None) if freq is None: raise ValueError('freq not specified and cannot be ' 'inferred from first element') if data.dtype != np.int64: if np.issubdtype(data.dtype, np.datetime64): data = dt64arr_to_periodarr(data, freq, tz) else: try: data = com._ensure_int64(data) except (TypeError, ValueError): data = com._ensure_object(data) data = _get_ordinals(data, freq) return data, freq
def _from_arraylike(cls, data, freq, tz): if not isinstance( data, (np.ndarray, PeriodIndex, DatetimeIndex, Int64Index)): if np.isscalar(data) or isinstance(data, Period): raise ValueError('PeriodIndex() must be called with a ' 'collection of some kind, %s was passed' % repr(data)) # other iterable of some kind if not isinstance(data, (list, tuple)): data = list(data) try: data = com._ensure_int64(data) if freq is None: raise ValueError('freq not specified') data = np.array([Period(x, freq=freq).ordinal for x in data], dtype=np.int64) except (TypeError, ValueError): data = com._ensure_object(data) if freq is None and len(data) > 0: freq = getattr(data[0], 'freq', None) if freq is None: raise ValueError('freq not specified and cannot be ' 'inferred from first element') data = _get_ordinals(data, freq) else: if isinstance(data, PeriodIndex): if freq is None or freq == data.freq: freq = data.freq data = data.values else: base1, _ = _gfc(data.freq) base2, _ = _gfc(freq) data = period.period_asfreq_arr(data.values, base1, base2, 1) else: if freq is None and len(data) > 0: freq = getattr(data[0], 'freq', None) if freq is None: raise ValueError('freq not specified and cannot be ' 'inferred from first element') if data.dtype != np.int64: if np.issubdtype(data.dtype, np.datetime64): data = dt64arr_to_periodarr(data, freq, tz) else: try: data = com._ensure_int64(data) except (TypeError, ValueError): data = com._ensure_object(data) data = _get_ordinals(data, freq) return data, freq
def _from_arraylike(cls, data, freq): if not isinstance(data, np.ndarray): if np.isscalar(data): raise ValueError('PeriodIndex() must be called with a ' 'collection of some kind, %s was passed' % repr(data)) elif isinstance(data, Period): raise ValueError('Data must be array of dates, strings, ' 'or Period objects') # other iterable of some kind if not isinstance(data, (list, tuple)): data = list(data) try: data = np.array(data, dtype='i8') except (TypeError, ValueError): data = np.array(data, dtype='O') if freq is None and len(data) > 0: freq = getattr(data[0], 'freq', None) if freq is None: raise ValueError(('freq not specified and cannot be inferred ' 'from first element')) data = _period_unbox_array(data, check=freq) else: if isinstance(data, PeriodIndex): if freq is None or freq == data.freq: freq = data.freq data = data.values else: base1, _ = _gfc(data.freq) base2, _ = _gfc(freq) data = plib.period_asfreq_arr(data.values, base1, base2, 1) else: if freq is None and len(data) > 0: freq = getattr(data[0], 'freq') if freq is None: raise ValueError(('freq not specified and cannot be ' 'inferred from first element')) if np.issubdtype(data.dtype, np.datetime64): data = dt64arr_to_periodarr(data, freq) elif data.dtype == np.int64: pass else: try: data = data.astype('i8') except (TypeError, ValueError): data = data.astype('O') data = _period_unbox_array(data, check=freq) return data, freq
def _from_arraylike(cls, data, freq, tz): if not isinstance(data, (np.ndarray, PeriodIndex, DatetimeIndex, Int64Index)): if is_scalar(data) or isinstance(data, Period): raise ValueError('PeriodIndex() must be called with a ' 'collection of some kind, %s was passed' % repr(data)) # other iterable of some kind if not isinstance(data, (list, tuple)): data = list(data) try: data = _ensure_int64(data) if freq is None: raise ValueError('freq not specified') data = np.array([Period(x, freq=freq) for x in data], dtype=np.int64) except (TypeError, ValueError): data = _ensure_object(data) if freq is None: freq = period.extract_freq(data) data = period.extract_ordinals(data, freq) else: if isinstance(data, PeriodIndex): if freq is None or freq == data.freq: freq = data.freq data = data.values else: base1, _ = _gfc(data.freq) base2, _ = _gfc(freq) data = period.period_asfreq_arr(data.values, base1, base2, 1) else: if freq is None and is_object_dtype(data): # must contain Period instance and thus extract ordinals freq = period.extract_freq(data) data = period.extract_ordinals(data, freq) if freq is None: msg = 'freq not specified and cannot be inferred' raise ValueError(msg) if data.dtype != np.int64: if np.issubdtype(data.dtype, np.datetime64): data = dt64arr_to_periodarr(data, freq, tz) else: try: data = _ensure_int64(data) except (TypeError, ValueError): data = _ensure_object(data) data = period.extract_ordinals(data, freq) return data, freq
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.tseries.period.PeriodIndex'> [2010, ..., 2015] Length: 6, Freq: A-DEC >>> pidx.asfreq('M') <class 'pandas.tseries.period.PeriodIndex'> [2010-12, ..., 2015-12] Length: 6, Freq: M >>> pidx.asfreq('M', how='S') <class 'pandas.tseries.period.PeriodIndex'> [2010-01, ..., 2015-01] Length: 6, Freq: M """ how = _validate_end_alias(how) freq = frequencies.get_standard_freq(freq) base1, mult1 = _gfc(self.freq) base2, mult2 = _gfc(freq) if mult2 != 1: raise ValueError('Only mult == 1 supported') end = how == 'E' new_data = period.period_asfreq_arr(self.values, base1, base2, end) return self._simple_new(new_data, self.name, freq=freq)
def asfreq(self, freq=None, how='E'): how = _validate_end_alias(how) freq = frequencies.get_standard_freq(freq) base1, mult1 = _gfc(self.freq) base2, mult2 = _gfc(freq) if mult2 != 1: raise ValueError('Only mult == 1 supported') end = how == 'E' new_data = period.period_asfreq_arr(self.values, base1, base2, end) return self._simple_new(new_data, self.name, freq=freq)
def asfreq(self, freq=None, how='E'): how = _validate_end_alias(how) freq = _freq_mod.get_standard_freq(freq) base1, mult1 = _gfc(self.freq) base2, mult2 = _gfc(freq) if mult2 != 1: raise ValueError('Only mult == 1 supported') end = how == 'E' new_data = plib.period_asfreq_arr(self.values, base1, base2, end) result = new_data.view(PeriodIndex) result.name = self.name result.freq = freq return result
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.tseries.period.PeriodIndex'> [2010, ..., 2015] Length: 6, Freq: A-DEC >>> pidx.asfreq('M') <class 'pandas.tseries.period.PeriodIndex'> [2010-12, ..., 2015-12] Length: 6, Freq: M >>> pidx.asfreq('M', how='S') <class 'pandas.tseries.period.PeriodIndex'> [2010-01, ..., 2015-01] Length: 6, Freq: M """ how = _validate_end_alias(how) freq = frequencies.get_standard_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: mask = asi8 == tslib.iNaT new_data[mask] = 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): 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)