/usr/lib/python3/dist-packages/pandas/tseries/period.py is in python3-pandas 0.14.1-2.
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import operator
from datetime import datetime, date
import numpy as np
from pandas.core.base import PandasObject
from pandas.tseries.frequencies import (get_freq_code as _gfc,
_month_numbers, FreqGroup)
from pandas.tseries.index import DatetimeIndex, Int64Index, Index
from pandas.core.base import DatetimeIndexOpsMixin
from pandas.tseries.tools import parse_time_string
import pandas.tseries.frequencies as _freq_mod
import pandas.core.common as com
from pandas.core.common import (isnull, _INT64_DTYPE, _maybe_box,
_values_from_object, ABCSeries)
from pandas import compat
from pandas.lib import Timestamp
import pandas.lib as lib
import pandas.tslib as tslib
import pandas.algos as _algos
from pandas.compat import zip, u
#---------------
# Period logic
def _period_field_accessor(name, alias):
def f(self):
base, mult = _gfc(self.freq)
return tslib.get_period_field(alias, self.ordinal, base)
f.__name__ = name
return property(f)
def _field_accessor(name, alias):
def f(self):
base, mult = _gfc(self.freq)
return tslib.get_period_field_arr(alias, self.values, base)
f.__name__ = name
return property(f)
class Period(PandasObject):
"""
Represents an period of time
Parameters
----------
value : Period or compat.string_types, default None
The time period represented (e.g., '4Q2005')
freq : str, default None
e.g., 'B' for businessday. Must be a singular rule-code (e.g. 5T is not
allowed).
year : int, default None
month : int, default 1
quarter : int, default None
day : int, default 1
hour : int, default 0
minute : int, default 0
second : int, default 0
"""
__slots__ = ['freq', 'ordinal']
_comparables = ['name','freqstr']
def __init__(self, value=None, freq=None, ordinal=None,
year=None, month=1, quarter=None, day=1,
hour=0, minute=0, second=0):
# freq points to a tuple (base, mult); base is one of the defined
# periods such as A, Q, etc. Every five minutes would be, e.g.,
# ('T', 5) but may be passed in as a string like '5T'
self.freq = None
# ordinal is the period offset from the gregorian proleptic epoch
self.ordinal = None
if ordinal is not None and value is not None:
raise ValueError(("Only value or ordinal but not both should be "
"given but not both"))
elif ordinal is not None:
if not com.is_integer(ordinal):
raise ValueError("Ordinal must be an integer")
if freq is None:
raise ValueError('Must supply freq for ordinal value')
self.ordinal = ordinal
elif value is None:
if freq is None:
raise ValueError("If value is None, freq cannot be None")
self.ordinal = _ordinal_from_fields(year, month, quarter, day,
hour, minute, second, freq)
elif isinstance(value, Period):
other = value
if freq is None or _gfc(freq) == _gfc(other.freq):
self.ordinal = other.ordinal
freq = other.freq
else:
converted = other.asfreq(freq)
self.ordinal = converted.ordinal
elif com._is_null_datelike_scalar(value) or value in tslib._nat_strings:
self.ordinal = tslib.iNaT
if freq is None:
raise ValueError("If value is NaT, freq cannot be None "
"because it cannot be inferred")
elif isinstance(value, compat.string_types) or com.is_integer(value):
if com.is_integer(value):
value = str(value)
value = value.upper()
dt, _, reso = parse_time_string(value, freq)
if freq is None:
try:
freq = _freq_mod.Resolution.get_freq(reso)
except KeyError:
raise ValueError("Invalid frequency or could not infer: %s" % reso)
elif isinstance(value, datetime):
dt = value
if freq is None:
raise ValueError('Must supply freq for datetime value')
elif isinstance(value, date):
dt = datetime(year=value.year, month=value.month, day=value.day)
if freq is None:
raise ValueError('Must supply freq for datetime value')
else:
msg = "Value must be Period, string, integer, or datetime"
raise ValueError(msg)
base, mult = _gfc(freq)
if mult != 1:
# TODO: Better error message - this is slightly confusing
raise ValueError('Only mult == 1 supported')
if self.ordinal is None:
self.ordinal = tslib.period_ordinal(dt.year, dt.month, dt.day,
dt.hour, dt.minute, dt.second, dt.microsecond, 0,
base)
self.freq = _freq_mod._get_freq_str(base)
def __eq__(self, other):
if isinstance(other, Period):
if other.freq != self.freq:
raise ValueError("Cannot compare non-conforming periods")
if self.ordinal == tslib.iNaT or other.ordinal == tslib.iNaT:
return False
return (self.ordinal == other.ordinal
and _gfc(self.freq) == _gfc(other.freq))
return NotImplemented
def __ne__(self, other):
return not self == other
def __hash__(self):
return hash((self.ordinal, self.freq))
def __add__(self, other):
if com.is_integer(other):
if self.ordinal == tslib.iNaT:
ordinal = self.ordinal
else:
ordinal = self.ordinal + other
return Period(ordinal=ordinal, freq=self.freq)
else: # pragma: no cover
return NotImplemented
def __sub__(self, other):
if com.is_integer(other):
if self.ordinal == tslib.iNaT:
ordinal = self.ordinal
else:
ordinal = self.ordinal - other
return Period(ordinal=ordinal, freq=self.freq)
if isinstance(other, Period):
if other.freq != self.freq:
raise ValueError("Cannot do arithmetic with "
"non-conforming periods")
if self.ordinal == tslib.iNaT or other.ordinal == tslib.iNaT:
return Period(ordinal=tslib.iNaT, freq=self.freq)
return self.ordinal - other.ordinal
else: # pragma: no cover
return NotImplemented
def _comp_method(func, name):
def f(self, other):
if isinstance(other, Period):
if other.freq != self.freq:
raise ValueError("Cannot compare non-conforming periods")
if self.ordinal == tslib.iNaT or other.ordinal == tslib.iNaT:
return False
return func(self.ordinal, other.ordinal)
else:
raise TypeError(other)
f.__name__ = name
return f
__lt__ = _comp_method(operator.lt, '__lt__')
__le__ = _comp_method(operator.le, '__le__')
__gt__ = _comp_method(operator.gt, '__gt__')
__ge__ = _comp_method(operator.ge, '__ge__')
def asfreq(self, freq, how='E'):
"""
Convert Period to desired frequency, either at the start or end of the
interval
Parameters
----------
freq : string
how : {'E', 'S', 'end', 'start'}, default 'end'
Start or end of the timespan
Returns
-------
resampled : Period
"""
how = _validate_end_alias(how)
base1, mult1 = _gfc(self.freq)
base2, mult2 = _gfc(freq)
if mult2 != 1:
raise ValueError('Only mult == 1 supported')
end = how == 'E'
new_ordinal = tslib.period_asfreq(self.ordinal, base1, base2, end)
return Period(ordinal=new_ordinal, freq=base2)
@property
def start_time(self):
return self.to_timestamp(how='S')
@property
def end_time(self):
if self.ordinal == tslib.iNaT:
ordinal = self.ordinal
else:
ordinal = (self + 1).start_time.value - 1
return Timestamp(ordinal)
def to_timestamp(self, freq=None, how='start', tz=None):
"""
Return the Timestamp representation of the Period at the target
frequency at the specified end (how) of the Period
Parameters
----------
freq : string or DateOffset, default is 'D' if self.freq is week or
longer and 'S' otherwise
Target frequency
how: str, default 'S' (start)
'S', 'E'. Can be aliased as case insensitive
'Start', 'Finish', 'Begin', 'End'
Returns
-------
Timestamp
"""
how = _validate_end_alias(how)
if freq is None:
base, mult = _gfc(self.freq)
freq = _freq_mod.get_to_timestamp_base(base)
base, mult = _gfc(freq)
val = self.asfreq(freq, how)
dt64 = tslib.period_ordinal_to_dt64(val.ordinal, base)
return Timestamp(dt64, tz=tz)
year = _period_field_accessor('year', 0)
month = _period_field_accessor('month', 3)
day = _period_field_accessor('day', 4)
hour = _period_field_accessor('hour', 5)
minute = _period_field_accessor('minute', 6)
second = _period_field_accessor('second', 7)
weekofyear = _period_field_accessor('week', 8)
week = weekofyear
dayofweek = _period_field_accessor('dayofweek', 10)
weekday = dayofweek
dayofyear = _period_field_accessor('dayofyear', 9)
quarter = _period_field_accessor('quarter', 2)
qyear = _period_field_accessor('qyear', 1)
@classmethod
def now(cls, freq=None):
return Period(datetime.now(), freq=freq)
def __repr__(self):
base, mult = _gfc(self.freq)
formatted = tslib.period_format(self.ordinal, base)
freqstr = _freq_mod._reverse_period_code_map[base]
if not compat.PY3:
encoding = com.get_option("display.encoding")
formatted = formatted.encode(encoding)
return "Period('%s', '%s')" % (formatted, freqstr)
def __unicode__(self):
"""
Return a string representation for a particular DataFrame
Invoked by unicode(df) in py2 only. Yields a Unicode String in both
py2/py3.
"""
base, mult = _gfc(self.freq)
formatted = tslib.period_format(self.ordinal, base)
value = ("%s" % formatted)
return value
def strftime(self, fmt):
"""
Returns the string representation of the :class:`Period`, depending
on the selected :keyword:`format`. :keyword:`format` must be a string
containing one or several directives. The method recognizes the same
directives as the :func:`time.strftime` function of the standard Python
distribution, as well as the specific additional directives ``%f``,
``%F``, ``%q``. (formatting & docs originally from scikits.timeries)
+-----------+--------------------------------+-------+
| Directive | Meaning | Notes |
+===========+================================+=======+
| ``%a`` | Locale's abbreviated weekday | |
| | name. | |
+-----------+--------------------------------+-------+
| ``%A`` | Locale's full weekday name. | |
+-----------+--------------------------------+-------+
| ``%b`` | Locale's abbreviated month | |
| | name. | |
+-----------+--------------------------------+-------+
| ``%B`` | Locale's full month name. | |
+-----------+--------------------------------+-------+
| ``%c`` | Locale's appropriate date and | |
| | time representation. | |
+-----------+--------------------------------+-------+
| ``%d`` | Day of the month as a decimal | |
| | number [01,31]. | |
+-----------+--------------------------------+-------+
| ``%f`` | 'Fiscal' year without a | \(1) |
| | century as a decimal number | |
| | [00,99] | |
+-----------+--------------------------------+-------+
| ``%F`` | 'Fiscal' year with a century | \(2) |
| | as a decimal number | |
+-----------+--------------------------------+-------+
| ``%H`` | Hour (24-hour clock) as a | |
| | decimal number [00,23]. | |
+-----------+--------------------------------+-------+
| ``%I`` | Hour (12-hour clock) as a | |
| | decimal number [01,12]. | |
+-----------+--------------------------------+-------+
| ``%j`` | Day of the year as a decimal | |
| | number [001,366]. | |
+-----------+--------------------------------+-------+
| ``%m`` | Month as a decimal number | |
| | [01,12]. | |
+-----------+--------------------------------+-------+
| ``%M`` | Minute as a decimal number | |
| | [00,59]. | |
+-----------+--------------------------------+-------+
| ``%p`` | Locale's equivalent of either | \(3) |
| | AM or PM. | |
+-----------+--------------------------------+-------+
| ``%q`` | Quarter as a decimal number | |
| | [01,04] | |
+-----------+--------------------------------+-------+
| ``%S`` | Second as a decimal number | \(4) |
| | [00,61]. | |
+-----------+--------------------------------+-------+
| ``%U`` | Week number of the year | \(5) |
| | (Sunday as the first day of | |
| | the week) as a decimal number | |
| | [00,53]. All days in a new | |
| | year preceding the first | |
| | Sunday are considered to be in | |
| | week 0. | |
+-----------+--------------------------------+-------+
| ``%w`` | Weekday as a decimal number | |
| | [0(Sunday),6]. | |
+-----------+--------------------------------+-------+
| ``%W`` | Week number of the year | \(5) |
| | (Monday as the first day of | |
| | the week) as a decimal number | |
| | [00,53]. All days in a new | |
| | year preceding the first | |
| | Monday are considered to be in | |
| | week 0. | |
+-----------+--------------------------------+-------+
| ``%x`` | Locale's appropriate date | |
| | representation. | |
+-----------+--------------------------------+-------+
| ``%X`` | Locale's appropriate time | |
| | representation. | |
+-----------+--------------------------------+-------+
| ``%y`` | Year without century as a | |
| | decimal number [00,99]. | |
+-----------+--------------------------------+-------+
| ``%Y`` | Year with century as a decimal | |
| | number. | |
+-----------+--------------------------------+-------+
| ``%Z`` | Time zone name (no characters | |
| | if no time zone exists). | |
+-----------+--------------------------------+-------+
| ``%%`` | A literal ``'%'`` character. | |
+-----------+--------------------------------+-------+
.. note::
(1)
The ``%f`` directive is the same as ``%y`` if the frequency is
not quarterly.
Otherwise, it corresponds to the 'fiscal' year, as defined by
the :attr:`qyear` attribute.
(2)
The ``%F`` directive is the same as ``%Y`` if the frequency is
not quarterly.
Otherwise, it corresponds to the 'fiscal' year, as defined by
the :attr:`qyear` attribute.
(3)
The ``%p`` directive only affects the output hour field
if the ``%I`` directive is used to parse the hour.
(4)
The range really is ``0`` to ``61``; this accounts for leap
seconds and the (very rare) double leap seconds.
(5)
The ``%U`` and ``%W`` directives are only used in calculations
when the day of the week and the year are specified.
.. rubric:: Examples
>>> a = Period(freq='Q@JUL', year=2006, quarter=1)
>>> a.strftime('%F-Q%q')
'2006-Q1'
>>> # Output the last month in the quarter of this date
>>> a.strftime('%b-%Y')
'Oct-2005'
>>>
>>> a = Period(freq='D', year=2001, month=1, day=1)
>>> a.strftime('%d-%b-%Y')
'01-Jan-2006'
>>> a.strftime('%b. %d, %Y was a %A')
'Jan. 01, 2001 was a Monday'
"""
base, mult = _gfc(self.freq)
return tslib.period_format(self.ordinal, base, fmt)
def _get_ordinals(data, freq):
f = lambda x: Period(x, freq=freq).ordinal
if isinstance(data[0], Period):
return tslib.extract_ordinals(data, freq)
else:
return lib.map_infer(data, f)
def dt64arr_to_periodarr(data, freq, tz):
if data.dtype != np.dtype('M8[ns]'):
raise ValueError('Wrong dtype: %s' % data.dtype)
base, mult = _gfc(freq)
return tslib.dt64arr_to_periodarr(data.view('i8'), base, tz)
# --- Period index sketch
def _period_index_cmp(opname, nat_result=False):
"""
Wrap comparison operations to convert datetime-like to datetime64
"""
def wrapper(self, other):
if isinstance(other, Period):
func = getattr(self.values, opname)
if other.freq != self.freq:
raise AssertionError("Frequencies must be equal")
result = func(other.ordinal)
elif isinstance(other, PeriodIndex):
if other.freq != self.freq:
raise AssertionError("Frequencies must be equal")
result = getattr(self.values, opname)(other.values)
mask = (com.mask_missing(self.values, tslib.iNaT) |
com.mask_missing(other.values, tslib.iNaT))
if mask.any():
result[mask] = nat_result
return result
else:
other = Period(other, freq=self.freq)
func = getattr(self.values, opname)
result = func(other.ordinal)
if other.ordinal == tslib.iNaT:
result.fill(nat_result)
mask = self.values == tslib.iNaT
if mask.any():
result[mask] = nat_result
return result
return wrapper
class PeriodIndex(DatetimeIndexOpsMixin, Int64Index):
"""
Immutable ndarray holding ordinal values indicating regular periods in
time such as particular years, quarters, months, etc. A value of 1 is the
period containing the Gregorian proleptic datetime Jan 1, 0001 00:00:00.
This ordinal representation is from the scikits.timeseries project.
For instance,
# construct period for day 1/1/1 and get the first second
i = Period(year=1,month=1,day=1,freq='D').asfreq('S', 'S')
i.ordinal
===> 1
Index keys are boxed to Period objects which carries the metadata (eg,
frequency information).
Parameters
----------
data : array-like (1-dimensional), optional
Optional period-like data to construct index with
dtype : NumPy dtype (default: i8)
copy : bool
Make a copy of input ndarray
freq : string or period object, optional
One of pandas period strings or corresponding objects
start : starting value, period-like, optional
If data is None, used as the start point in generating regular
period data.
periods : int, optional, > 0
Number of periods to generate, if generating index. Takes precedence
over end argument
end : end value, period-like, optional
If periods is none, generated index will extend to first conforming
period on or just past end argument
year : int, array, or Series, default None
month : int, array, or Series, default None
quarter : int, array, or Series, default None
day : int, array, or Series, default None
hour : int, array, or Series, default None
minute : int, array, or Series, default None
second : int, array, or Series, default None
tz : object, default None
Timezone for converting datetime64 data to Periods
Examples
--------
>>> idx = PeriodIndex(year=year_arr, quarter=q_arr)
>>> idx2 = PeriodIndex(start='2000', end='2010', freq='A')
"""
_box_scalars = True
_allow_period_index_ops = True
__eq__ = _period_index_cmp('__eq__')
__ne__ = _period_index_cmp('__ne__', nat_result=True)
__lt__ = _period_index_cmp('__lt__')
__gt__ = _period_index_cmp('__gt__')
__le__ = _period_index_cmp('__le__')
__ge__ = _period_index_cmp('__ge__')
def __new__(cls, data=None, ordinal=None, freq=None, start=None, end=None,
periods=None, copy=False, name=None, year=None, month=None,
quarter=None, day=None, hour=None, minute=None, second=None,
tz=None):
freq = _freq_mod.get_standard_freq(freq)
if periods is not None:
if com.is_float(periods):
periods = int(periods)
elif not com.is_integer(periods):
raise ValueError('Periods must be a number, got %s' %
str(periods))
if data is None:
if ordinal is not None:
data = np.asarray(ordinal, dtype=np.int64)
else:
fields = [year, month, quarter, day, hour, minute, second]
data, freq = cls._generate_range(start, end, periods,
freq, fields)
else:
ordinal, freq = cls._from_arraylike(data, freq, tz)
data = np.array(ordinal, dtype=np.int64, copy=False)
subarr = data.view(cls)
subarr.name = name
subarr.freq = freq
return subarr
@classmethod
def _generate_range(cls, start, end, periods, freq, fields):
field_count = com._count_not_none(*fields)
if com._count_not_none(start, end) > 0:
if field_count > 0:
raise ValueError('Can either instantiate from fields '
'or endpoints, but not both')
subarr, freq = _get_ordinal_range(start, end, periods, freq)
elif field_count > 0:
y, mth, q, d, h, minute, s = fields
subarr, freq = _range_from_fields(year=y, month=mth, quarter=q,
day=d, hour=h, minute=minute,
second=s, freq=freq)
else:
raise ValueError('Not enough parameters to construct '
'Period range')
return subarr, freq
@classmethod
def _from_arraylike(cls, data, freq, tz):
if not isinstance(data, np.ndarray):
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 = tslib.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
@classmethod
def _simple_new(cls, values, name, freq=None, **kwargs):
result = values.view(cls)
result.name = name
result.freq = freq
return result
@property
def _na_value(self):
return self._box_func(tslib.iNaT)
def __contains__(self, key):
if not isinstance(key, Period) or key.freq != self.freq:
if isinstance(key, compat.string_types):
try:
self.get_loc(key)
return True
except Exception:
return False
return False
return key.ordinal in self._engine
@property
def _box_func(self):
return lambda x: Period(ordinal=x, freq=self.freq)
def asof_locs(self, where, mask):
"""
where : array of timestamps
mask : array of booleans where data is not NA
"""
where_idx = where
if isinstance(where_idx, DatetimeIndex):
where_idx = PeriodIndex(where_idx.values, freq=self.freq)
locs = self.values[mask].searchsorted(where_idx.values, side='right')
locs = np.where(locs > 0, locs - 1, 0)
result = np.arange(len(self))[mask].take(locs)
first = mask.argmax()
result[(locs == 0) & (where_idx.values < self.values[first])] = -1
return result
def _array_values(self):
return self.asobject
def astype(self, dtype):
dtype = np.dtype(dtype)
if dtype == np.object_:
return Index(np.array(list(self), dtype), dtype)
elif dtype == _INT64_DTYPE:
return Index(self.values, dtype)
raise ValueError('Cannot cast PeriodIndex to dtype %s' % dtype)
def __iter__(self):
for val in self.values:
yield Period(ordinal=val, freq=self.freq)
def searchsorted(self, key, side='left'):
if isinstance(key, compat.string_types):
key = Period(key, freq=self.freq).ordinal
return self.values.searchsorted(key, side=side)
@property
def is_all_dates(self):
return True
@property
def is_full(self):
"""
Returns True if there are any missing periods from start to end
"""
if len(self) == 0:
return True
if not self.is_monotonic:
raise ValueError('Index is not monotonic')
values = self.values
return ((values[1:] - values[:-1]) < 2).all()
@property
def freqstr(self):
return self.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 = tslib.period_asfreq_arr(self.values, base1, base2, end)
return self._simple_new(new_data, self.name, freq=freq)
def to_datetime(self, dayfirst=False):
return self.to_timestamp()
_year = _field_accessor('year', 0)
_month = _field_accessor('month', 3)
_day = _field_accessor('day', 4)
_hour = _field_accessor('hour', 5)
_minute = _field_accessor('minute', 6)
_second = _field_accessor('second', 7)
_weekofyear = _field_accessor('week', 8)
_week = _weekofyear
_dayofweek = _field_accessor('dayofweek', 10)
_weekday = _dayofweek
_dayofyear = day_of_year = _field_accessor('dayofyear', 9)
_quarter = _field_accessor('quarter', 2)
_qyear = _field_accessor('qyear', 1)
# Try to run function on index first, and then on elements of index
# Especially important for group-by functionality
def map(self, f):
try:
result = f(self)
if not isinstance(result, np.ndarray):
raise TypeError
return result
except Exception:
return _algos.arrmap_object(self.asobject, f)
def _get_object_array(self):
freq = self.freq
boxfunc = lambda x: Period(ordinal=x, freq=freq)
boxer = np.frompyfunc(boxfunc, 1, 1)
return boxer(self.values)
def _mpl_repr(self):
# how to represent ourselves to matplotlib
return self._get_object_array()
def equals(self, other):
"""
Determines if two Index objects contain the same elements.
"""
if self.is_(other):
return True
return np.array_equal(self.asi8, other.asi8)
def to_timestamp(self, freq=None, how='start'):
"""
Cast to DatetimeIndex
Parameters
----------
freq : string or DateOffset, default 'D' for week or longer, 'S'
otherwise
Target frequency
how : {'s', 'e', 'start', 'end'}
Returns
-------
DatetimeIndex
"""
how = _validate_end_alias(how)
if freq is None:
base, mult = _gfc(self.freq)
freq = _freq_mod.get_to_timestamp_base(base)
base, mult = _gfc(freq)
new_data = self.asfreq(freq, how)
new_data = tslib.periodarr_to_dt64arr(new_data.values, base)
return DatetimeIndex(new_data, freq='infer', name=self.name)
def shift(self, n):
"""
Specialized shift which produces an PeriodIndex
Parameters
----------
n : int
Periods to shift by
freq : freq string
Returns
-------
shifted : PeriodIndex
"""
mask = self.values == tslib.iNaT
values = self.values + n
values[mask] = tslib.iNaT
return PeriodIndex(data=values, name=self.name, freq=self.freq)
def __add__(self, other):
try:
return self.shift(other)
except TypeError:
# self.values + other raises TypeError for invalid input
return NotImplemented
def __sub__(self, other):
try:
return self.shift(-other)
except TypeError:
return NotImplemented
@property
def inferred_type(self):
# b/c data is represented as ints make sure we can't have ambiguous
# indexing
return 'period'
def get_value(self, series, key):
"""
Fast lookup of value from 1-dimensional ndarray. Only use this if you
know what you're doing
"""
s = _values_from_object(series)
try:
return _maybe_box(self, super(PeriodIndex, self).get_value(s, key), series, key)
except (KeyError, IndexError):
try:
asdt, parsed, reso = parse_time_string(key, self.freq)
grp = _freq_mod._infer_period_group(reso)
freqn = _freq_mod._period_group(self.freq)
vals = self.values
# if our data is higher resolution than requested key, slice
if grp < freqn:
iv = Period(asdt, freq=(grp, 1))
ord1 = iv.asfreq(self.freq, how='S').ordinal
ord2 = iv.asfreq(self.freq, how='E').ordinal
if ord2 < vals[0] or ord1 > vals[-1]:
raise KeyError(key)
pos = np.searchsorted(self.values, [ord1, ord2])
key = slice(pos[0], pos[1] + 1)
return series[key]
elif grp == freqn:
key = Period(asdt, freq=self.freq).ordinal
return _maybe_box(self, self._engine.get_value(s, key), series, key)
else:
raise KeyError(key)
except TypeError:
pass
key = Period(key, self.freq).ordinal
return _maybe_box(self, self._engine.get_value(s, key), series, key)
def get_loc(self, key):
"""
Get integer location for requested label
Returns
-------
loc : int
"""
try:
return self._engine.get_loc(key)
except KeyError:
try:
asdt, parsed, reso = parse_time_string(key, self.freq)
key = asdt
except TypeError:
pass
key = Period(key, self.freq)
try:
return self._engine.get_loc(key.ordinal)
except KeyError:
raise KeyError(key)
def slice_locs(self, start=None, end=None):
"""
Index.slice_locs, customized to handle partial ISO-8601 string slicing
"""
if isinstance(start, compat.string_types) or isinstance(end, compat.string_types):
try:
if start:
start_loc = self._get_string_slice(start).start
else:
start_loc = 0
if end:
end_loc = self._get_string_slice(end).stop
else:
end_loc = len(self)
return start_loc, end_loc
except KeyError:
pass
if isinstance(start, datetime) and isinstance(end, datetime):
ordinals = self.values
t1 = Period(start, freq=self.freq)
t2 = Period(end, freq=self.freq)
left = ordinals.searchsorted(t1.ordinal, side='left')
right = ordinals.searchsorted(t2.ordinal, side='right')
return left, right
return Int64Index.slice_locs(self, start, end)
def _get_string_slice(self, key):
if not self.is_monotonic:
raise ValueError('Partial indexing only valid for '
'ordered time series')
key, parsed, reso = parse_time_string(key, self.freq)
grp = _freq_mod._infer_period_group(reso)
freqn = _freq_mod._period_group(self.freq)
if reso == 'year':
t1 = Period(year=parsed.year, freq='A')
elif reso == 'month':
t1 = Period(year=parsed.year, month=parsed.month, freq='M')
elif reso == 'quarter':
q = (parsed.month - 1) // 3 + 1
t1 = Period(year=parsed.year, quarter=q, freq='Q-DEC')
elif reso == 'day' and grp < freqn:
t1 = Period(year=parsed.year, month=parsed.month, day=parsed.day,
freq='D')
elif reso == 'hour' and grp < freqn:
t1 = Period(year=parsed.year, month=parsed.month, day=parsed.day,
hour=parsed.hour, freq='H')
elif reso == 'minute' and grp < freqn:
t1 = Period(year=parsed.year, month=parsed.month, day=parsed.day,
hour=parsed.hour, minute=parsed.minute, freq='T')
elif reso == 'second' and grp < freqn:
t1 = Period(year=parsed.year, month=parsed.month, day=parsed.day,
hour=parsed.hour, minute=parsed.minute, second=parsed.second,
freq='S')
else:
raise KeyError(key)
ordinals = self.values
t2 = t1.asfreq(self.freq, how='end')
t1 = t1.asfreq(self.freq, how='start')
left = ordinals.searchsorted(t1.ordinal, side='left')
right = ordinals.searchsorted(t2.ordinal, side='right')
return slice(left, right)
def join(self, other, how='left', level=None, return_indexers=False):
"""
See Index.join
"""
self._assert_can_do_setop(other)
result = Int64Index.join(self, other, how=how, level=level,
return_indexers=return_indexers)
if return_indexers:
result, lidx, ridx = result
return self._apply_meta(result), lidx, ridx
return self._apply_meta(result)
def _assert_can_do_setop(self, other):
if not isinstance(other, PeriodIndex):
raise ValueError('can only call with other PeriodIndex-ed objects')
if self.freq != other.freq:
raise ValueError('Only like-indexed PeriodIndexes compatible '
'for join (for now)')
def _wrap_union_result(self, other, result):
name = self.name if self.name == other.name else None
result = self._apply_meta(result)
result.name = name
return result
def _apply_meta(self, rawarr):
if not isinstance(rawarr, PeriodIndex):
rawarr = rawarr.view(PeriodIndex)
rawarr.freq = self.freq
return rawarr
def __getitem__(self, key):
"""Override numpy.ndarray's __getitem__ method to work as desired"""
arr_idx = self.view(np.ndarray)
if np.isscalar(key):
val = arr_idx[key]
return Period(ordinal=val, freq=self.freq)
else:
if com._is_bool_indexer(key):
key = np.asarray(key)
result = arr_idx[key]
if result.ndim > 1:
# MPL kludge
# values = np.asarray(list(values), dtype=object)
# return values.reshape(result.shape)
return PeriodIndex(result, name=self.name, freq=self.freq)
return PeriodIndex(result, name=self.name, freq=self.freq)
def _format_with_header(self, header, **kwargs):
return header + self._format_native_types(**kwargs)
def _format_native_types(self, na_rep=u('NaT'), **kwargs):
values = np.array(list(self), dtype=object)
mask = isnull(self.values)
values[mask] = na_rep
imask = ~mask
values[imask] = np.array([u('%s') % dt for dt in values[imask]])
return values.tolist()
def __array_finalize__(self, obj):
if not self.ndim: # pragma: no cover
return self.item()
self.freq = getattr(obj, 'freq', None)
self.name = getattr(obj, 'name', None)
self._reset_identity()
def _format_footer(self):
tagline = 'Length: %d, Freq: %s'
return tagline % (len(self), self.freqstr)
def take(self, indices, axis=None):
"""
Analogous to ndarray.take
"""
indices = com._ensure_platform_int(indices)
taken = self.values.take(indices, axis=axis)
return self._simple_new(taken, self.name, freq=self.freq)
def append(self, other):
"""
Append a collection of Index options together
Parameters
----------
other : Index or list/tuple of indices
Returns
-------
appended : Index
"""
name = self.name
to_concat = [self]
if isinstance(other, (list, tuple)):
to_concat = to_concat + list(other)
else:
to_concat.append(other)
for obj in to_concat:
if isinstance(obj, Index) and obj.name != name:
name = None
break
to_concat = self._ensure_compat_concat(to_concat)
if isinstance(to_concat[0], PeriodIndex):
if len(set([x.freq for x in to_concat])) > 1:
# box
to_concat = [x.asobject for x in to_concat]
else:
cat_values = np.concatenate([x.values for x in to_concat])
return PeriodIndex(cat_values, freq=self.freq, name=name)
to_concat = [x.values if isinstance(x, Index) else x
for x in to_concat]
return Index(com._concat_compat(to_concat), name=name)
def __reduce__(self):
"""Necessary for making this object picklable"""
object_state = list(np.ndarray.__reduce__(self))
subclass_state = (self.name, self.freq)
object_state[2] = (object_state[2], subclass_state)
return tuple(object_state)
def __setstate__(self, state):
"""Necessary for making this object picklable"""
if len(state) == 2:
nd_state, own_state = state
np.ndarray.__setstate__(self, nd_state)
self.name = own_state[0]
try: # backcompat
self.freq = own_state[1]
except:
pass
else: # pragma: no cover
np.ndarray.__setstate__(self, state)
def _get_ordinal_range(start, end, periods, freq):
if com._count_not_none(start, end, periods) < 2:
raise ValueError('Must specify 2 of start, end, periods')
if start is not None:
start = Period(start, freq)
if end is not None:
end = Period(end, freq)
is_start_per = isinstance(start, Period)
is_end_per = isinstance(end, Period)
if is_start_per and is_end_per and start.freq != end.freq:
raise ValueError('Start and end must have same freq')
if ((is_start_per and start.ordinal == tslib.iNaT) or
(is_end_per and end.ordinal == tslib.iNaT)):
raise ValueError('Start and end must not be NaT')
if freq is None:
if is_start_per:
freq = start.freq
elif is_end_per:
freq = end.freq
else: # pragma: no cover
raise ValueError('Could not infer freq from start/end')
if periods is not None:
if start is None:
data = np.arange(end.ordinal - periods + 1,
end.ordinal + 1,
dtype=np.int64)
else:
data = np.arange(start.ordinal, start.ordinal + periods,
dtype=np.int64)
else:
data = np.arange(start.ordinal, end.ordinal + 1, dtype=np.int64)
return data, freq
def _range_from_fields(year=None, month=None, quarter=None, day=None,
hour=None, minute=None, second=None, freq=None):
if hour is None:
hour = 0
if minute is None:
minute = 0
if second is None:
second = 0
if day is None:
day = 1
ordinals = []
if quarter is not None:
if freq is None:
freq = 'Q'
base = FreqGroup.FR_QTR
else:
base, mult = _gfc(freq)
if mult != 1:
raise ValueError('Only mult == 1 supported')
if base != FreqGroup.FR_QTR:
raise AssertionError("base must equal FR_QTR")
year, quarter = _make_field_arrays(year, quarter)
for y, q in zip(year, quarter):
y, m = _quarter_to_myear(y, q, freq)
val = tslib.period_ordinal(y, m, 1, 1, 1, 1, 0, 0, base)
ordinals.append(val)
else:
base, mult = _gfc(freq)
if mult != 1:
raise ValueError('Only mult == 1 supported')
arrays = _make_field_arrays(year, month, day, hour, minute, second)
for y, mth, d, h, mn, s in zip(*arrays):
ordinals.append(tslib.period_ordinal(y, mth, d, h, mn, s, 0, 0, base))
return np.array(ordinals, dtype=np.int64), freq
def _make_field_arrays(*fields):
length = None
for x in fields:
if isinstance(x, (list, np.ndarray, ABCSeries)):
if length is not None and len(x) != length:
raise ValueError('Mismatched Period array lengths')
elif length is None:
length = len(x)
arrays = [np.asarray(x) if isinstance(x, (np.ndarray, list, ABCSeries))
else np.repeat(x, length) for x in fields]
return arrays
def _ordinal_from_fields(year, month, quarter, day, hour, minute,
second, freq):
base, mult = _gfc(freq)
if mult != 1:
raise ValueError('Only mult == 1 supported')
if quarter is not None:
year, month = _quarter_to_myear(year, quarter, freq)
return tslib.period_ordinal(year, month, day, hour, minute, second, 0, 0, base)
def _quarter_to_myear(year, quarter, freq):
if quarter is not None:
if quarter <= 0 or quarter > 4:
raise ValueError('Quarter must be 1 <= q <= 4')
mnum = _month_numbers[_freq_mod._get_rule_month(freq)] + 1
month = (mnum + (quarter - 1) * 3) % 12 + 1
if month > mnum:
year -= 1
return year, month
def _validate_end_alias(how):
how_dict = {'S': 'S', 'E': 'E',
'START': 'S', 'FINISH': 'E',
'BEGIN': 'S', 'END': 'E'}
how = how_dict.get(str(how).upper())
if how not in set(['S', 'E']):
raise ValueError('How must be one of S or E')
return how
def pnow(freq=None):
return Period(datetime.now(), freq=freq)
def period_range(start=None, end=None, periods=None, freq='D', name=None):
"""
Return a fixed frequency datetime index, with day (calendar) as the default
frequency
Parameters
----------
start :
end :
periods : int, default None
Number of periods in the index
freq : str/DateOffset, default 'D'
Frequency alias
name : str, default None
Name for the resulting PeriodIndex
Returns
-------
prng : PeriodIndex
"""
return PeriodIndex(start=start, end=end, periods=periods,
freq=freq, name=name)
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