from __future__ import annotations
from typing import TYPE_CHECKING
import numpy as np
from pandas._libs import lib
from pandas._libs.algos import unique_deltas
from pandas._libs.tslibs import (
Timestamp,
get_unit_from_dtype,
periods_per_day,
tz_convert_from_utc,
)
from pandas._libs.tslibs.ccalendar import (
DAYS,
MONTH_ALIASES,
MONTH_NUMBERS,
MONTHS,
int_to_weekday,
)
from pandas._libs.tslibs.fields import (
build_field_sarray,
month_position_check,
)
from pandas._libs.tslibs.offsets import (
DateOffset,
Day,
to_offset,
)
from pandas._libs.tslibs.parsing import get_rule_month
from pandas.util._decorators import cache_readonly
from pandas.core.dtypes.common import is_numeric_dtype
from pandas.core.dtypes.dtypes import (
DatetimeTZDtype,
PeriodDtype,
)
from pandas.core.dtypes.generic import (
ABCIndex,
ABCSeries,
)
from pandas.core.algorithms import unique
if TYPE_CHECKING:
from pandas._typing import npt
from pandas import (
DatetimeIndex,
Series,
TimedeltaIndex,
)
from pandas.core.arrays.datetimelike import DatetimeLikeArrayMixin
# ---------------------------------------------------------------------
# Offset names ("time rules") and related functions
_offset_to_period_map = {
"WEEKDAY": "D",
"EOM": "M",
"BM": "M",
"BQS": "Q",
"QS": "Q",
"BQ": "Q",
"BA": "A",
"AS": "A",
"BAS": "A",
"MS": "M",
"D": "D",
"B": "B",
"T": "T",
"S": "S",
"L": "L",
"U": "U",
"N": "N",
"H": "H",
"Q": "Q",
"A": "A",
"W": "W",
"M": "M",
"Y": "A",
"BY": "A",
"YS": "A",
"BYS": "A",
}
_need_suffix = ["QS", "BQ", "BQS", "YS", "AS", "BY", "BA", "BYS", "BAS"]
for _prefix in _need_suffix:
for _m in MONTHS:
key = f"{_prefix}-{_m}"
_offset_to_period_map[key] = _offset_to_period_map[_prefix]
for _prefix in ["A", "Q"]:
for _m in MONTHS:
_alias = f"{_prefix}-{_m}"
_offset_to_period_map[_alias] = _alias
for _d in DAYS:
_offset_to_period_map[f"W-{_d}"] = f"W-{_d}"
def get_period_alias(offset_str: str) -> str | None:
"""
Alias to closest period strings BQ->Q etc.
"""
return _offset_to_period_map.get(offset_str, None)
# ---------------------------------------------------------------------
# Period codes
[docs]def infer_freq(
index: DatetimeIndex | TimedeltaIndex | Series | DatetimeLikeArrayMixin,
) -> str | None:
"""
Infer the most likely frequency given the input index.
Parameters
----------
index : DatetimeIndex, TimedeltaIndex, Series or array-like
If passed a Series will use the values of the series (NOT THE INDEX).
Returns
-------
str or None
None if no discernible frequency.
Raises
------
TypeError
If the index is not datetime-like.
ValueError
If there are fewer than three values.
Examples
--------
>>> idx = pd.date_range(start='2020/12/01', end='2020/12/30', periods=30)
>>> pd.infer_freq(idx)
'D'
"""
from pandas.core.api import DatetimeIndex
if isinstance(index, ABCSeries):
values = index._values
if not (
lib.is_np_dtype(values.dtype, "mM")
or isinstance(values.dtype, DatetimeTZDtype)
or values.dtype == object
):
raise TypeError(
"cannot infer freq from a non-convertible dtype "
f"on a Series of {index.dtype}"
)
index = values
inferer: _FrequencyInferer
if not hasattr(index, "dtype"):
pass
elif isinstance(index.dtype, PeriodDtype):
raise TypeError(
"PeriodIndex given. Check the `freq` attribute "
"instead of using infer_freq."
)
elif lib.is_np_dtype(index.dtype, "m"):
# Allow TimedeltaIndex and TimedeltaArray
inferer = _TimedeltaFrequencyInferer(index)
return inferer.get_freq()
elif is_numeric_dtype(index.dtype):
raise TypeError(
f"cannot infer freq from a non-convertible index of dtype {index.dtype}"
)
if not isinstance(index, DatetimeIndex):
index = DatetimeIndex(index)
inferer = _FrequencyInferer(index)
return inferer.get_freq()
class _FrequencyInferer:
"""
Not sure if I can avoid the state machine here
"""
def __init__(self, index) -> None:
self.index = index
self.i8values = index.asi8
# For get_unit_from_dtype we need the dtype to the underlying ndarray,
# which for tz-aware is not the same as index.dtype
if isinstance(index, ABCIndex):
# error: Item "ndarray[Any, Any]" of "Union[ExtensionArray,
# ndarray[Any, Any]]" has no attribute "_ndarray"
self._creso = get_unit_from_dtype(
index._data._ndarray.dtype # type: ignore[union-attr]
)
else:
# otherwise we have DTA/TDA
self._creso = get_unit_from_dtype(index._ndarray.dtype)
# This moves the values, which are implicitly in UTC, to the
# the timezone so they are in local time
if hasattr(index, "tz"):
if index.tz is not None:
self.i8values = tz_convert_from_utc(
self.i8values, index.tz, reso=self._creso
)
if len(index) < 3:
raise ValueError("Need at least 3 dates to infer frequency")
self.is_monotonic = (
self.index._is_monotonic_increasing or self.index._is_monotonic_decreasing
)
@cache_readonly
def deltas(self) -> npt.NDArray[np.int64]:
return unique_deltas(self.i8values)
@cache_readonly
def deltas_asi8(self) -> npt.NDArray[np.int64]:
# NB: we cannot use self.i8values here because we may have converted
# the tz in __init__
return unique_deltas(self.index.asi8)
@cache_readonly
def is_unique(self) -> bool:
return len(self.deltas) == 1
@cache_readonly
def is_unique_asi8(self) -> bool:
return len(self.deltas_asi8) == 1
def get_freq(self) -> str | None:
"""
Find the appropriate frequency string to describe the inferred
frequency of self.i8values
Returns
-------
str or None
"""
if not self.is_monotonic or not self.index._is_unique:
return None
delta = self.deltas[0]
ppd = periods_per_day(self._creso)
if delta and _is_multiple(delta, ppd):
return self._infer_daily_rule()
# Business hourly, maybe. 17: one day / 65: one weekend
if self.hour_deltas in ([1, 17], [1, 65], [1, 17, 65]):
return "BH"
# Possibly intraday frequency. Here we use the
# original .asi8 values as the modified values
# will not work around DST transitions. See #8772
if not self.is_unique_asi8:
return None
delta = self.deltas_asi8[0]
pph = ppd // 24
ppm = pph // 60
pps = ppm // 60
if _is_multiple(delta, pph):
# Hours
return _maybe_add_count("H", delta / pph)
elif _is_multiple(delta, ppm):
# Minutes
return _maybe_add_count("T", delta / ppm)
elif _is_multiple(delta, pps):
# Seconds
return _maybe_add_count("S", delta / pps)
elif _is_multiple(delta, (pps // 1000)):
# Milliseconds
return _maybe_add_count("L", delta / (pps // 1000))
elif _is_multiple(delta, (pps // 1_000_000)):
# Microseconds
return _maybe_add_count("U", delta / (pps // 1_000_000))
else:
# Nanoseconds
return _maybe_add_count("N", delta)
@cache_readonly
def day_deltas(self) -> list[int]:
ppd = periods_per_day(self._creso)
return [x / ppd for x in self.deltas]
@cache_readonly
def hour_deltas(self) -> list[int]:
pph = periods_per_day(self._creso) // 24
return [x / pph for x in self.deltas]
@cache_readonly
def fields(self) -> np.ndarray: # structured array of fields
return build_field_sarray(self.i8values, reso=self._creso)
@cache_readonly
def rep_stamp(self) -> Timestamp:
return Timestamp(self.i8values[0])
def month_position_check(self) -> str | None:
return month_position_check(self.fields, self.index.dayofweek)
@cache_readonly
def mdiffs(self) -> npt.NDArray[np.int64]:
nmonths = self.fields["Y"] * 12 + self.fields["M"]
return unique_deltas(nmonths.astype("i8"))
@cache_readonly
def ydiffs(self) -> npt.NDArray[np.int64]:
return unique_deltas(self.fields["Y"].astype("i8"))
def _infer_daily_rule(self) -> str | None:
annual_rule = self._get_annual_rule()
if annual_rule:
nyears = self.ydiffs[0]
month = MONTH_ALIASES[self.rep_stamp.month]
alias = f"{annual_rule}-{month}"
return _maybe_add_count(alias, nyears)
quarterly_rule = self._get_quarterly_rule()
if quarterly_rule:
nquarters = self.mdiffs[0] / 3
mod_dict = {0: 12, 2: 11, 1: 10}
month = MONTH_ALIASES[mod_dict[self.rep_stamp.month % 3]]
alias = f"{quarterly_rule}-{month}"
return _maybe_add_count(alias, nquarters)
monthly_rule = self._get_monthly_rule()
if monthly_rule:
return _maybe_add_count(monthly_rule, self.mdiffs[0])
if self.is_unique:
return self._get_daily_rule()
if self._is_business_daily():
return "B"
wom_rule = self._get_wom_rule()
if wom_rule:
return wom_rule
return None
def _get_daily_rule(self) -> str | None:
ppd = periods_per_day(self._creso)
days = self.deltas[0] / ppd
if days % 7 == 0:
# Weekly
wd = int_to_weekday[self.rep_stamp.weekday()]
alias = f"W-{wd}"
return _maybe_add_count(alias, days / 7)
else:
return _maybe_add_count("D", days)
def _get_annual_rule(self) -> str | None:
if len(self.ydiffs) > 1:
return None
if len(unique(self.fields["M"])) > 1:
return None
pos_check = self.month_position_check()
if pos_check is None:
return None
else:
return {"cs": "AS", "bs": "BAS", "ce": "A", "be": "BA"}.get(pos_check)
def _get_quarterly_rule(self) -> str | None:
if len(self.mdiffs) > 1:
return None
if not self.mdiffs[0] % 3 == 0:
return None
pos_check = self.month_position_check()
if pos_check is None:
return None
else:
return {"cs": "QS", "bs": "BQS", "ce": "Q", "be": "BQ"}.get(pos_check)
def _get_monthly_rule(self) -> str | None:
if len(self.mdiffs) > 1:
return None
pos_check = self.month_position_check()
if pos_check is None:
return None
else:
return {"cs": "MS", "bs": "BMS", "ce": "M", "be": "BM"}.get(pos_check)
def _is_business_daily(self) -> bool:
# quick check: cannot be business daily
if self.day_deltas != [1, 3]:
return False
# probably business daily, but need to confirm
first_weekday = self.index[0].weekday()
shifts = np.diff(self.i8values)
ppd = periods_per_day(self._creso)
shifts = np.floor_divide(shifts, ppd)
weekdays = np.mod(first_weekday + np.cumsum(shifts), 7)
return bool(
np.all(
((weekdays == 0) & (shifts == 3))
| ((weekdays > 0) & (weekdays <= 4) & (shifts == 1))
)
)
def _get_wom_rule(self) -> str | None:
weekdays = unique(self.index.weekday)
if len(weekdays) > 1:
return None
week_of_months = unique((self.index.day - 1) // 7)
# Only attempt to infer up to WOM-4. See #9425
week_of_months = week_of_months[week_of_months < 4]
if len(week_of_months) == 0 or len(week_of_months) > 1:
return None
# get which week
week = week_of_months[0] + 1
wd = int_to_weekday[weekdays[0]]
return f"WOM-{week}{wd}"
class _TimedeltaFrequencyInferer(_FrequencyInferer):
def _infer_daily_rule(self):
if self.is_unique:
return self._get_daily_rule()
def _is_multiple(us, mult: int) -> bool:
return us % mult == 0
def _maybe_add_count(base: str, count: float) -> str:
if count != 1:
assert count == int(count)
count = int(count)
return f"{count}{base}"
else:
return base
# ----------------------------------------------------------------------
# Frequency comparison
def is_subperiod(source, target) -> bool:
"""
Returns True if downsampling is possible between source and target
frequencies
Parameters
----------
source : str or DateOffset
Frequency converting from
target : str or DateOffset
Frequency converting to
Returns
-------
bool
"""
if target is None or source is None:
return False
source = _maybe_coerce_freq(source)
target = _maybe_coerce_freq(target)
if _is_annual(target):
if _is_quarterly(source):
return _quarter_months_conform(
get_rule_month(source), get_rule_month(target)
)
return source in {"D", "C", "B", "M", "H", "T", "S", "L", "U", "N"}
elif _is_quarterly(target):
return source in {"D", "C", "B", "M", "H", "T", "S", "L", "U", "N"}
elif _is_monthly(target):
return source in {"D", "C", "B", "H", "T", "S", "L", "U", "N"}
elif _is_weekly(target):
return source in {target, "D", "C", "B", "H", "T", "S", "L", "U", "N"}
elif target == "B":
return source in {"B", "H", "T", "S", "L", "U", "N"}
elif target == "C":
return source in {"C", "H", "T", "S", "L", "U", "N"}
elif target == "D":
return source in {"D", "H", "T", "S", "L", "U", "N"}
elif target == "H":
return source in {"H", "T", "S", "L", "U", "N"}
elif target == "T":
return source in {"T", "S", "L", "U", "N"}
elif target == "S":
return source in {"S", "L", "U", "N"}
elif target == "L":
return source in {"L", "U", "N"}
elif target == "U":
return source in {"U", "N"}
elif target == "N":
return source in {"N"}
else:
return False
def is_superperiod(source, target) -> bool:
"""
Returns True if upsampling is possible between source and target
frequencies
Parameters
----------
source : str or DateOffset
Frequency converting from
target : str or DateOffset
Frequency converting to
Returns
-------
bool
"""
if target is None or source is None:
return False
source = _maybe_coerce_freq(source)
target = _maybe_coerce_freq(target)
if _is_annual(source):
if _is_annual(target):
return get_rule_month(source) == get_rule_month(target)
if _is_quarterly(target):
smonth = get_rule_month(source)
tmonth = get_rule_month(target)
return _quarter_months_conform(smonth, tmonth)
return target in {"D", "C", "B", "M", "H", "T", "S", "L", "U", "N"}
elif _is_quarterly(source):
return target in {"D", "C", "B", "M", "H", "T", "S", "L", "U", "N"}
elif _is_monthly(source):
return target in {"D", "C", "B", "H", "T", "S", "L", "U", "N"}
elif _is_weekly(source):
return target in {source, "D", "C", "B", "H", "T", "S", "L", "U", "N"}
elif source == "B":
return target in {"D", "C", "B", "H", "T", "S", "L", "U", "N"}
elif source == "C":
return target in {"D", "C", "B", "H", "T", "S", "L", "U", "N"}
elif source == "D":
return target in {"D", "C", "B", "H", "T", "S", "L", "U", "N"}
elif source == "H":
return target in {"H", "T", "S", "L", "U", "N"}
elif source == "T":
return target in {"T", "S", "L", "U", "N"}
elif source == "S":
return target in {"S", "L", "U", "N"}
elif source == "L":
return target in {"L", "U", "N"}
elif source == "U":
return target in {"U", "N"}
elif source == "N":
return target in {"N"}
else:
return False
def _maybe_coerce_freq(code) -> str:
"""we might need to coerce a code to a rule_code
and uppercase it
Parameters
----------
source : str or DateOffset
Frequency converting from
Returns
-------
str
"""
assert code is not None
if isinstance(code, DateOffset):
code = code.rule_code
return code.upper()
def _quarter_months_conform(source: str, target: str) -> bool:
snum = MONTH_NUMBERS[source]
tnum = MONTH_NUMBERS[target]
return snum % 3 == tnum % 3
def _is_annual(rule: str) -> bool:
rule = rule.upper()
return rule == "A" or rule.startswith("A-")
def _is_quarterly(rule: str) -> bool:
rule = rule.upper()
return rule == "Q" or rule.startswith(("Q-", "BQ"))
def _is_monthly(rule: str) -> bool:
rule = rule.upper()
return rule in ("M", "BM")
def _is_weekly(rule: str) -> bool:
rule = rule.upper()
return rule == "W" or rule.startswith("W-")
__all__ = [
"Day",
"get_period_alias",
"infer_freq",
"is_subperiod",
"is_superperiod",
"to_offset",
]