1269 lines
42 KiB
Python
1269 lines
42 KiB
Python
from __future__ import annotations
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|
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from collections import abc
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from datetime import datetime
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from functools import partial
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from itertools import islice
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from typing import (
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TYPE_CHECKING,
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Callable,
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Hashable,
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List,
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Tuple,
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TypeVar,
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Union,
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overload,
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||
)
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import warnings
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import numpy as np
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from pandas._libs import tslib
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from pandas._libs.tslibs import (
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OutOfBoundsDatetime,
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Timedelta,
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Timestamp,
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conversion,
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iNaT,
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nat_strings,
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parsing,
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)
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from pandas._libs.tslibs.parsing import ( # noqa:F401
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DateParseError,
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format_is_iso,
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guess_datetime_format,
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)
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from pandas._libs.tslibs.strptime import array_strptime
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from pandas._typing import (
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AnyArrayLike,
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ArrayLike,
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Timezone,
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)
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from pandas.util._exceptions import find_stack_level
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from pandas.core.dtypes.common import (
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ensure_object,
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is_datetime64_dtype,
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is_datetime64_ns_dtype,
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is_datetime64tz_dtype,
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is_float,
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is_integer,
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is_integer_dtype,
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is_list_like,
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is_numeric_dtype,
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is_scalar,
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)
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from pandas.core.dtypes.generic import (
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ABCDataFrame,
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ABCSeries,
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)
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from pandas.core.dtypes.missing import notna
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from pandas.arrays import (
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DatetimeArray,
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IntegerArray,
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)
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from pandas.core import algorithms
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from pandas.core.algorithms import unique
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from pandas.core.arrays.datetimes import (
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maybe_convert_dtype,
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objects_to_datetime64ns,
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tz_to_dtype,
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)
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from pandas.core.construction import extract_array
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from pandas.core.indexes.base import Index
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from pandas.core.indexes.datetimes import DatetimeIndex
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if TYPE_CHECKING:
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from pandas._libs.tslibs.nattype import NaTType
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from pandas import Series
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# ---------------------------------------------------------------------
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# types used in annotations
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ArrayConvertible = Union[List, Tuple, AnyArrayLike, "Series"]
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Scalar = Union[int, float, str]
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DatetimeScalar = TypeVar("DatetimeScalar", Scalar, datetime)
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DatetimeScalarOrArrayConvertible = Union[DatetimeScalar, ArrayConvertible]
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start_caching_at = 50
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# ---------------------------------------------------------------------
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def _guess_datetime_format_for_array(arr, **kwargs):
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# Try to guess the format based on the first non-NaN element
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non_nan_elements = notna(arr).nonzero()[0]
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if len(non_nan_elements):
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return guess_datetime_format(arr[non_nan_elements[0]], **kwargs)
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def should_cache(
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arg: ArrayConvertible, unique_share: float = 0.7, check_count: int | None = None
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) -> bool:
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"""
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Decides whether to do caching.
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If the percent of unique elements among `check_count` elements less
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than `unique_share * 100` then we can do caching.
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Parameters
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----------
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arg: listlike, tuple, 1-d array, Series
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unique_share: float, default=0.7, optional
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0 < unique_share < 1
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check_count: int, optional
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0 <= check_count <= len(arg)
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Returns
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-------
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do_caching: bool
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Notes
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-----
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By default for a sequence of less than 50 items in size, we don't do
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caching; for the number of elements less than 5000, we take ten percent of
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all elements to check for a uniqueness share; if the sequence size is more
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than 5000, then we check only the first 500 elements.
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All constants were chosen empirically by.
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"""
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do_caching = True
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# default realization
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if check_count is None:
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# in this case, the gain from caching is negligible
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if len(arg) <= start_caching_at:
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return False
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if len(arg) <= 5000:
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check_count = len(arg) // 10
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else:
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check_count = 500
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else:
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assert (
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0 <= check_count <= len(arg)
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), "check_count must be in next bounds: [0; len(arg)]"
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if check_count == 0:
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return False
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assert 0 < unique_share < 1, "unique_share must be in next bounds: (0; 1)"
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try:
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# We can't cache if the items are not hashable.
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unique_elements = set(islice(arg, check_count))
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except TypeError:
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return False
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if len(unique_elements) > check_count * unique_share:
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do_caching = False
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return do_caching
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def _maybe_cache(
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arg: ArrayConvertible,
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format: str | None,
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cache: bool,
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convert_listlike: Callable,
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) -> Series:
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"""
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Create a cache of unique dates from an array of dates
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Parameters
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----------
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arg : listlike, tuple, 1-d array, Series
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format : string
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Strftime format to parse time
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cache : bool
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True attempts to create a cache of converted values
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convert_listlike : function
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Conversion function to apply on dates
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Returns
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-------
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cache_array : Series
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Cache of converted, unique dates. Can be empty
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"""
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from pandas import Series
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cache_array = Series(dtype=object)
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if cache:
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# Perform a quicker unique check
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if not should_cache(arg):
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return cache_array
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unique_dates = unique(arg)
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if len(unique_dates) < len(arg):
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cache_dates = convert_listlike(unique_dates, format)
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cache_array = Series(cache_dates, index=unique_dates)
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# GH#39882 and GH#35888 in case of None and NaT we get duplicates
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if not cache_array.index.is_unique:
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cache_array = cache_array[~cache_array.index.duplicated()]
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return cache_array
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def _box_as_indexlike(
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dt_array: ArrayLike, utc: bool | None = None, name: Hashable = None
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) -> Index:
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"""
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Properly boxes the ndarray of datetimes to DatetimeIndex
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if it is possible or to generic Index instead
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Parameters
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----------
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dt_array: 1-d array
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Array of datetimes to be wrapped in an Index.
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tz : object
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None or 'utc'
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name : string, default None
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Name for a resulting index
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Returns
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-------
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result : datetime of converted dates
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- DatetimeIndex if convertible to sole datetime64 type
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- general Index otherwise
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"""
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if is_datetime64_dtype(dt_array):
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tz = "utc" if utc else None
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return DatetimeIndex(dt_array, tz=tz, name=name)
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return Index(dt_array, name=name, dtype=dt_array.dtype)
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def _convert_and_box_cache(
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arg: DatetimeScalarOrArrayConvertible,
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cache_array: Series,
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name: str | None = None,
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) -> Index:
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"""
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Convert array of dates with a cache and wrap the result in an Index.
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Parameters
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----------
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arg : integer, float, string, datetime, list, tuple, 1-d array, Series
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cache_array : Series
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Cache of converted, unique dates
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name : string, default None
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Name for a DatetimeIndex
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Returns
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-------
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result : Index-like of converted dates
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"""
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from pandas import Series
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result = Series(arg).map(cache_array)
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return _box_as_indexlike(result._values, utc=None, name=name)
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def _return_parsed_timezone_results(result: np.ndarray, timezones, tz, name) -> Index:
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"""
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Return results from array_strptime if a %z or %Z directive was passed.
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Parameters
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----------
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result : ndarray[int64]
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int64 date representations of the dates
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timezones : ndarray
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pytz timezone objects
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tz : object
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None or pytz timezone object
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name : string, default None
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Name for a DatetimeIndex
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Returns
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-------
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tz_result : Index-like of parsed dates with timezone
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"""
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tz_results = np.array(
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[Timestamp(res).tz_localize(zone) for res, zone in zip(result, timezones)]
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)
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if tz is not None:
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# Convert to the same tz
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tz_results = np.array([tz_result.tz_convert(tz) for tz_result in tz_results])
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return Index(tz_results, name=name)
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def _convert_listlike_datetimes(
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arg,
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format: str | None,
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name: Hashable = None,
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tz: Timezone | None = None,
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unit: str | None = None,
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||
errors: str = "raise",
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infer_datetime_format: bool = False,
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||
dayfirst: bool | None = None,
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yearfirst: bool | None = None,
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||
exact: bool = True,
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||
):
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"""
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Helper function for to_datetime. Performs the conversions of 1D listlike
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of dates
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Parameters
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----------
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arg : list, tuple, ndarray, Series, Index
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||
date to be parsed
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name : object
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||
None or string for the Index name
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tz : object
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||
None or 'utc'
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||
unit : str
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||
None or string of the frequency of the passed data
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||
errors : str
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||
error handing behaviors from to_datetime, 'raise', 'coerce', 'ignore'
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||
infer_datetime_format : bool, default False
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inferring format behavior from to_datetime
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dayfirst : bool
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||
dayfirst parsing behavior from to_datetime
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||
yearfirst : bool
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yearfirst parsing behavior from to_datetime
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exact : bool, default True
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exact format matching behavior from to_datetime
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||
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Returns
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-------
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Index-like of parsed dates
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"""
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if isinstance(arg, (list, tuple)):
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arg = np.array(arg, dtype="O")
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arg_dtype = getattr(arg, "dtype", None)
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# these are shortcutable
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||
if is_datetime64tz_dtype(arg_dtype):
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if not isinstance(arg, (DatetimeArray, DatetimeIndex)):
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return DatetimeIndex(arg, tz=tz, name=name)
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if tz == "utc":
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arg = arg.tz_convert(None).tz_localize(tz)
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return arg
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|
||
elif is_datetime64_ns_dtype(arg_dtype):
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if not isinstance(arg, (DatetimeArray, DatetimeIndex)):
|
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try:
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return DatetimeIndex(arg, tz=tz, name=name)
|
||
except ValueError:
|
||
pass
|
||
elif tz:
|
||
# DatetimeArray, DatetimeIndex
|
||
return arg.tz_localize(tz)
|
||
|
||
return arg
|
||
|
||
elif unit is not None:
|
||
if format is not None:
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raise ValueError("cannot specify both format and unit")
|
||
return _to_datetime_with_unit(arg, unit, name, tz, errors)
|
||
elif getattr(arg, "ndim", 1) > 1:
|
||
raise TypeError(
|
||
"arg must be a string, datetime, list, tuple, 1-d array, or Series"
|
||
)
|
||
|
||
# warn if passing timedelta64, raise for PeriodDtype
|
||
# NB: this must come after unit transformation
|
||
orig_arg = arg
|
||
try:
|
||
arg, _ = maybe_convert_dtype(arg, copy=False)
|
||
except TypeError:
|
||
if errors == "coerce":
|
||
npvalues = np.array(["NaT"], dtype="datetime64[ns]").repeat(len(arg))
|
||
return DatetimeIndex(npvalues, name=name)
|
||
elif errors == "ignore":
|
||
idx = Index(arg, name=name)
|
||
return idx
|
||
raise
|
||
|
||
arg = ensure_object(arg)
|
||
require_iso8601 = False
|
||
|
||
if infer_datetime_format and format is None:
|
||
format = _guess_datetime_format_for_array(arg, dayfirst=dayfirst)
|
||
|
||
if format is not None:
|
||
# There is a special fast-path for iso8601 formatted
|
||
# datetime strings, so in those cases don't use the inferred
|
||
# format because this path makes process slower in this
|
||
# special case
|
||
format_is_iso8601 = format_is_iso(format)
|
||
if format_is_iso8601:
|
||
require_iso8601 = not infer_datetime_format
|
||
format = None
|
||
|
||
if format is not None:
|
||
res = _to_datetime_with_format(
|
||
arg, orig_arg, name, tz, format, exact, errors, infer_datetime_format
|
||
)
|
||
if res is not None:
|
||
return res
|
||
|
||
assert format is None or infer_datetime_format
|
||
utc = tz == "utc"
|
||
result, tz_parsed = objects_to_datetime64ns(
|
||
arg,
|
||
dayfirst=dayfirst,
|
||
yearfirst=yearfirst,
|
||
utc=utc,
|
||
errors=errors,
|
||
require_iso8601=require_iso8601,
|
||
allow_object=True,
|
||
)
|
||
|
||
if tz_parsed is not None:
|
||
# We can take a shortcut since the datetime64 numpy array
|
||
# is in UTC
|
||
dta = DatetimeArray(result, dtype=tz_to_dtype(tz_parsed))
|
||
return DatetimeIndex._simple_new(dta, name=name)
|
||
|
||
utc = tz == "utc"
|
||
return _box_as_indexlike(result, utc=utc, name=name)
|
||
|
||
|
||
def _array_strptime_with_fallback(
|
||
arg,
|
||
name,
|
||
tz,
|
||
fmt: str,
|
||
exact: bool,
|
||
errors: str,
|
||
infer_datetime_format: bool,
|
||
) -> Index | None:
|
||
"""
|
||
Call array_strptime, with fallback behavior depending on 'errors'.
|
||
"""
|
||
utc = tz == "utc"
|
||
|
||
try:
|
||
result, timezones = array_strptime(arg, fmt, exact=exact, errors=errors)
|
||
if "%Z" in fmt or "%z" in fmt:
|
||
return _return_parsed_timezone_results(result, timezones, tz, name)
|
||
except OutOfBoundsDatetime:
|
||
if errors == "raise":
|
||
raise
|
||
elif errors == "coerce":
|
||
result = np.empty(arg.shape, dtype="M8[ns]")
|
||
iresult = result.view("i8")
|
||
iresult.fill(iNaT)
|
||
else:
|
||
result = arg
|
||
except ValueError:
|
||
# if fmt was inferred, try falling back
|
||
# to array_to_datetime - terminate here
|
||
# for specified formats
|
||
if not infer_datetime_format:
|
||
if errors == "raise":
|
||
raise
|
||
elif errors == "coerce":
|
||
result = np.empty(arg.shape, dtype="M8[ns]")
|
||
iresult = result.view("i8")
|
||
iresult.fill(iNaT)
|
||
else:
|
||
result = arg
|
||
else:
|
||
# Indicates to the caller to fallback to objects_to_datetime64ns
|
||
return None
|
||
|
||
return _box_as_indexlike(result, utc=utc, name=name)
|
||
|
||
|
||
def _to_datetime_with_format(
|
||
arg,
|
||
orig_arg,
|
||
name,
|
||
tz,
|
||
fmt: str,
|
||
exact: bool,
|
||
errors: str,
|
||
infer_datetime_format: bool,
|
||
) -> Index | None:
|
||
"""
|
||
Try parsing with the given format, returning None on failure.
|
||
"""
|
||
result = None
|
||
try:
|
||
# shortcut formatting here
|
||
if fmt == "%Y%m%d":
|
||
# pass orig_arg as float-dtype may have been converted to
|
||
# datetime64[ns]
|
||
orig_arg = ensure_object(orig_arg)
|
||
try:
|
||
# may return None without raising
|
||
result = _attempt_YYYYMMDD(orig_arg, errors=errors)
|
||
except (ValueError, TypeError, OutOfBoundsDatetime) as err:
|
||
raise ValueError(
|
||
"cannot convert the input to '%Y%m%d' date format"
|
||
) from err
|
||
if result is not None:
|
||
utc = tz == "utc"
|
||
return _box_as_indexlike(result, utc=utc, name=name)
|
||
|
||
# fallback
|
||
res = _array_strptime_with_fallback(
|
||
arg, name, tz, fmt, exact, errors, infer_datetime_format
|
||
)
|
||
return res
|
||
|
||
except ValueError as err:
|
||
# Fallback to try to convert datetime objects if timezone-aware
|
||
# datetime objects are found without passing `utc=True`
|
||
try:
|
||
values, tz = conversion.datetime_to_datetime64(arg)
|
||
dta = DatetimeArray(values, dtype=tz_to_dtype(tz))
|
||
return DatetimeIndex._simple_new(dta, name=name)
|
||
except (ValueError, TypeError):
|
||
raise err
|
||
|
||
|
||
def _to_datetime_with_unit(arg, unit, name, tz, errors: str) -> Index:
|
||
"""
|
||
to_datetime specalized to the case where a 'unit' is passed.
|
||
"""
|
||
arg = extract_array(arg, extract_numpy=True)
|
||
|
||
# GH#30050 pass an ndarray to tslib.array_with_unit_to_datetime
|
||
# because it expects an ndarray argument
|
||
if isinstance(arg, IntegerArray):
|
||
arr = arg.astype(f"datetime64[{unit}]")
|
||
tz_parsed = None
|
||
else:
|
||
arg = np.asarray(arg)
|
||
arr, tz_parsed = tslib.array_with_unit_to_datetime(arg, unit, errors=errors)
|
||
|
||
if errors == "ignore":
|
||
# Index constructor _may_ infer to DatetimeIndex
|
||
result = Index._with_infer(arr, name=name)
|
||
else:
|
||
result = DatetimeIndex(arr, name=name)
|
||
|
||
if not isinstance(result, DatetimeIndex):
|
||
return result
|
||
|
||
# GH#23758: We may still need to localize the result with tz
|
||
# GH#25546: Apply tz_parsed first (from arg), then tz (from caller)
|
||
# result will be naive but in UTC
|
||
result = result.tz_localize("UTC").tz_convert(tz_parsed)
|
||
|
||
if tz is not None:
|
||
if result.tz is None:
|
||
result = result.tz_localize(tz)
|
||
else:
|
||
result = result.tz_convert(tz)
|
||
return result
|
||
|
||
|
||
def _adjust_to_origin(arg, origin, unit):
|
||
"""
|
||
Helper function for to_datetime.
|
||
Adjust input argument to the specified origin
|
||
|
||
Parameters
|
||
----------
|
||
arg : list, tuple, ndarray, Series, Index
|
||
date to be adjusted
|
||
origin : 'julian' or Timestamp
|
||
origin offset for the arg
|
||
unit : str
|
||
passed unit from to_datetime, must be 'D'
|
||
|
||
Returns
|
||
-------
|
||
ndarray or scalar of adjusted date(s)
|
||
"""
|
||
if origin == "julian":
|
||
original = arg
|
||
j0 = Timestamp(0).to_julian_date()
|
||
if unit != "D":
|
||
raise ValueError("unit must be 'D' for origin='julian'")
|
||
try:
|
||
arg = arg - j0
|
||
except TypeError as err:
|
||
raise ValueError(
|
||
"incompatible 'arg' type for given 'origin'='julian'"
|
||
) from err
|
||
|
||
# preemptively check this for a nice range
|
||
j_max = Timestamp.max.to_julian_date() - j0
|
||
j_min = Timestamp.min.to_julian_date() - j0
|
||
if np.any(arg > j_max) or np.any(arg < j_min):
|
||
raise OutOfBoundsDatetime(
|
||
f"{original} is Out of Bounds for origin='julian'"
|
||
)
|
||
else:
|
||
# arg must be numeric
|
||
if not (
|
||
(is_scalar(arg) and (is_integer(arg) or is_float(arg)))
|
||
or is_numeric_dtype(np.asarray(arg))
|
||
):
|
||
raise ValueError(
|
||
f"'{arg}' is not compatible with origin='{origin}'; "
|
||
"it must be numeric with a unit specified"
|
||
)
|
||
|
||
# we are going to offset back to unix / epoch time
|
||
try:
|
||
offset = Timestamp(origin)
|
||
except OutOfBoundsDatetime as err:
|
||
raise OutOfBoundsDatetime(f"origin {origin} is Out of Bounds") from err
|
||
except ValueError as err:
|
||
raise ValueError(
|
||
f"origin {origin} cannot be converted to a Timestamp"
|
||
) from err
|
||
|
||
if offset.tz is not None:
|
||
raise ValueError(f"origin offset {offset} must be tz-naive")
|
||
td_offset = offset - Timestamp(0)
|
||
|
||
# convert the offset to the unit of the arg
|
||
# this should be lossless in terms of precision
|
||
ioffset = td_offset // Timedelta(1, unit=unit)
|
||
|
||
# scalars & ndarray-like can handle the addition
|
||
if is_list_like(arg) and not isinstance(arg, (ABCSeries, Index, np.ndarray)):
|
||
arg = np.asarray(arg)
|
||
arg = arg + ioffset
|
||
return arg
|
||
|
||
|
||
@overload
|
||
def to_datetime(
|
||
arg: DatetimeScalar,
|
||
errors: str = ...,
|
||
dayfirst: bool = ...,
|
||
yearfirst: bool = ...,
|
||
utc: bool | None = ...,
|
||
format: str | None = ...,
|
||
exact: bool = ...,
|
||
unit: str | None = ...,
|
||
infer_datetime_format: bool = ...,
|
||
origin=...,
|
||
cache: bool = ...,
|
||
) -> DatetimeScalar | NaTType:
|
||
...
|
||
|
||
|
||
@overload
|
||
def to_datetime(
|
||
arg: Series,
|
||
errors: str = ...,
|
||
dayfirst: bool = ...,
|
||
yearfirst: bool = ...,
|
||
utc: bool | None = ...,
|
||
format: str | None = ...,
|
||
exact: bool = ...,
|
||
unit: str | None = ...,
|
||
infer_datetime_format: bool = ...,
|
||
origin=...,
|
||
cache: bool = ...,
|
||
) -> Series:
|
||
...
|
||
|
||
|
||
@overload
|
||
def to_datetime(
|
||
arg: list | tuple | np.ndarray,
|
||
errors: str = ...,
|
||
dayfirst: bool = ...,
|
||
yearfirst: bool = ...,
|
||
utc: bool | None = ...,
|
||
format: str | None = ...,
|
||
exact: bool = ...,
|
||
unit: str | None = ...,
|
||
infer_datetime_format: bool = ...,
|
||
origin=...,
|
||
cache: bool = ...,
|
||
) -> DatetimeIndex:
|
||
...
|
||
|
||
|
||
def to_datetime(
|
||
arg: DatetimeScalarOrArrayConvertible,
|
||
errors: str = "raise",
|
||
dayfirst: bool = False,
|
||
yearfirst: bool = False,
|
||
utc: bool | None = None,
|
||
format: str | None = None,
|
||
exact: bool = True,
|
||
unit: str | None = None,
|
||
infer_datetime_format: bool = False,
|
||
origin="unix",
|
||
cache: bool = True,
|
||
) -> DatetimeIndex | Series | DatetimeScalar | NaTType | None:
|
||
"""
|
||
Convert argument to datetime.
|
||
|
||
This function converts a scalar, array-like, :class:`Series` or
|
||
:class:`DataFrame`/dict-like to a pandas datetime object.
|
||
|
||
Parameters
|
||
----------
|
||
arg : int, float, str, datetime, list, tuple, 1-d array, Series, DataFrame/dict-like
|
||
The object to convert to a datetime. If a :class:`DataFrame` is provided, the
|
||
method expects minimally the following columns: :const:`"year"`,
|
||
:const:`"month"`, :const:`"day"`.
|
||
errors : {'ignore', 'raise', 'coerce'}, default 'raise'
|
||
- If :const:`'raise'`, then invalid parsing will raise an exception.
|
||
- If :const:`'coerce'`, then invalid parsing will be set as :const:`NaT`.
|
||
- If :const:`'ignore'`, then invalid parsing will return the input.
|
||
dayfirst : bool, default False
|
||
Specify a date parse order if `arg` is str or is list-like.
|
||
If :const:`True`, parses dates with the day first, e.g. :const:`"10/11/12"`
|
||
is parsed as :const:`2012-11-10`.
|
||
|
||
.. warning::
|
||
|
||
``dayfirst=True`` is not strict, but will prefer to parse
|
||
with day first. If a delimited date string cannot be parsed in
|
||
accordance with the given `dayfirst` option, e.g.
|
||
``to_datetime(['31-12-2021'])``, then a warning will be shown.
|
||
|
||
yearfirst : bool, default False
|
||
Specify a date parse order if `arg` is str or is list-like.
|
||
|
||
- If :const:`True` parses dates with the year first, e.g.
|
||
:const:`"10/11/12"` is parsed as :const:`2010-11-12`.
|
||
- If both `dayfirst` and `yearfirst` are :const:`True`, `yearfirst` is
|
||
preceded (same as :mod:`dateutil`).
|
||
|
||
.. warning::
|
||
|
||
``yearfirst=True`` is not strict, but will prefer to parse
|
||
with year first.
|
||
|
||
utc : bool, default None
|
||
Control timezone-related parsing, localization and conversion.
|
||
|
||
- If :const:`True`, the function *always* returns a timezone-aware
|
||
UTC-localized :class:`Timestamp`, :class:`Series` or
|
||
:class:`DatetimeIndex`. To do this, timezone-naive inputs are
|
||
*localized* as UTC, while timezone-aware inputs are *converted* to UTC.
|
||
|
||
- If :const:`False` (default), inputs will not be coerced to UTC.
|
||
Timezone-naive inputs will remain naive, while timezone-aware ones
|
||
will keep their time offsets. Limitations exist for mixed
|
||
offsets (typically, daylight savings), see :ref:`Examples
|
||
<to_datetime_tz_examples>` section for details.
|
||
|
||
See also: pandas general documentation about `timezone conversion and
|
||
localization
|
||
<https://pandas.pydata.org/pandas-docs/stable/user_guide/timeseries.html
|
||
#time-zone-handling>`_.
|
||
|
||
format : str, default None
|
||
The strftime to parse time, e.g. :const:`"%d/%m/%Y"`. Note that
|
||
:const:`"%f"` will parse all the way up to nanoseconds. See
|
||
`strftime documentation
|
||
<https://docs.python.org/3/library/datetime.html
|
||
#strftime-and-strptime-behavior>`_ for more information on choices.
|
||
exact : bool, default True
|
||
Control how `format` is used:
|
||
|
||
- If :const:`True`, require an exact `format` match.
|
||
- If :const:`False`, allow the `format` to match anywhere in the target
|
||
string.
|
||
|
||
unit : str, default 'ns'
|
||
The unit of the arg (D,s,ms,us,ns) denote the unit, which is an
|
||
integer or float number. This will be based off the origin.
|
||
Example, with ``unit='ms'`` and ``origin='unix'`` (the default), this
|
||
would calculate the number of milliseconds to the unix epoch start.
|
||
infer_datetime_format : bool, default False
|
||
If :const:`True` and no `format` is given, attempt to infer the format
|
||
of the datetime strings based on the first non-NaN element,
|
||
and if it can be inferred, switch to a faster method of parsing them.
|
||
In some cases this can increase the parsing speed by ~5-10x.
|
||
origin : scalar, default 'unix'
|
||
Define the reference date. The numeric values would be parsed as number
|
||
of units (defined by `unit`) since this reference date.
|
||
|
||
- If :const:`'unix'` (or POSIX) time; origin is set to 1970-01-01.
|
||
- If :const:`'julian'`, unit must be :const:`'D'`, and origin is set to
|
||
beginning of Julian Calendar. Julian day number :const:`0` is assigned
|
||
to the day starting at noon on January 1, 4713 BC.
|
||
- If Timestamp convertible, origin is set to Timestamp identified by
|
||
origin.
|
||
cache : bool, default True
|
||
If :const:`True`, use a cache of unique, converted dates to apply the
|
||
datetime conversion. May produce significant speed-up when parsing
|
||
duplicate date strings, especially ones with timezone offsets. The cache
|
||
is only used when there are at least 50 values. The presence of
|
||
out-of-bounds values will render the cache unusable and may slow down
|
||
parsing.
|
||
|
||
.. versionchanged:: 0.25.0
|
||
changed default value from :const:`False` to :const:`True`.
|
||
|
||
Returns
|
||
-------
|
||
datetime
|
||
If parsing succeeded.
|
||
Return type depends on input (types in parenthesis correspond to
|
||
fallback in case of unsuccessful timezone or out-of-range timestamp
|
||
parsing):
|
||
|
||
- scalar: :class:`Timestamp` (or :class:`datetime.datetime`)
|
||
- array-like: :class:`DatetimeIndex` (or :class:`Series` with
|
||
:class:`object` dtype containing :class:`datetime.datetime`)
|
||
- Series: :class:`Series` of :class:`datetime64` dtype (or
|
||
:class:`Series` of :class:`object` dtype containing
|
||
:class:`datetime.datetime`)
|
||
- DataFrame: :class:`Series` of :class:`datetime64` dtype (or
|
||
:class:`Series` of :class:`object` dtype containing
|
||
:class:`datetime.datetime`)
|
||
|
||
Raises
|
||
------
|
||
ParserError
|
||
When parsing a date from string fails.
|
||
ValueError
|
||
When another datetime conversion error happens. For example when one
|
||
of 'year', 'month', day' columns is missing in a :class:`DataFrame`, or
|
||
when a Timezone-aware :class:`datetime.datetime` is found in an array-like
|
||
of mixed time offsets, and ``utc=False``.
|
||
|
||
See Also
|
||
--------
|
||
DataFrame.astype : Cast argument to a specified dtype.
|
||
to_timedelta : Convert argument to timedelta.
|
||
convert_dtypes : Convert dtypes.
|
||
|
||
Notes
|
||
-----
|
||
|
||
Many input types are supported, and lead to different output types:
|
||
|
||
- **scalars** can be int, float, str, datetime object (from stdlib :mod:`datetime`
|
||
module or :mod:`numpy`). They are converted to :class:`Timestamp` when
|
||
possible, otherwise they are converted to :class:`datetime.datetime`.
|
||
None/NaN/null scalars are converted to :const:`NaT`.
|
||
|
||
- **array-like** can contain int, float, str, datetime objects. They are
|
||
converted to :class:`DatetimeIndex` when possible, otherwise they are
|
||
converted to :class:`Index` with :class:`object` dtype, containing
|
||
:class:`datetime.datetime`. None/NaN/null entries are converted to
|
||
:const:`NaT` in both cases.
|
||
|
||
- **Series** are converted to :class:`Series` with :class:`datetime64`
|
||
dtype when possible, otherwise they are converted to :class:`Series` with
|
||
:class:`object` dtype, containing :class:`datetime.datetime`. None/NaN/null
|
||
entries are converted to :const:`NaT` in both cases.
|
||
|
||
- **DataFrame/dict-like** are converted to :class:`Series` with
|
||
:class:`datetime64` dtype. For each row a datetime is created from assembling
|
||
the various dataframe columns. Column keys can be common abbreviations
|
||
like [‘year’, ‘month’, ‘day’, ‘minute’, ‘second’, ‘ms’, ‘us’, ‘ns’]) or
|
||
plurals of the same.
|
||
|
||
The following causes are responsible for :class:`datetime.datetime` objects
|
||
being returned (possibly inside an :class:`Index` or a :class:`Series` with
|
||
:class:`object` dtype) instead of a proper pandas designated type
|
||
(:class:`Timestamp`, :class:`DatetimeIndex` or :class:`Series`
|
||
with :class:`datetime64` dtype):
|
||
|
||
- when any input element is before :const:`Timestamp.min` or after
|
||
:const:`Timestamp.max`, see `timestamp limitations
|
||
<https://pandas.pydata.org/pandas-docs/stable/user_guide/timeseries.html
|
||
#timeseries-timestamp-limits>`_.
|
||
|
||
- when ``utc=False`` (default) and the input is an array-like or
|
||
:class:`Series` containing mixed naive/aware datetime, or aware with mixed
|
||
time offsets. Note that this happens in the (quite frequent) situation when
|
||
the timezone has a daylight savings policy. In that case you may wish to
|
||
use ``utc=True``.
|
||
|
||
Examples
|
||
--------
|
||
|
||
**Handling various input formats**
|
||
|
||
Assembling a datetime from multiple columns of a :class:`DataFrame`. The keys
|
||
can be common abbreviations like ['year', 'month', 'day', 'minute', 'second',
|
||
'ms', 'us', 'ns']) or plurals of the same
|
||
|
||
>>> df = pd.DataFrame({'year': [2015, 2016],
|
||
... 'month': [2, 3],
|
||
... 'day': [4, 5]})
|
||
>>> pd.to_datetime(df)
|
||
0 2015-02-04
|
||
1 2016-03-05
|
||
dtype: datetime64[ns]
|
||
|
||
Passing ``infer_datetime_format=True`` can often-times speedup a parsing
|
||
if its not an ISO8601 format exactly, but in a regular format.
|
||
|
||
>>> s = pd.Series(['3/11/2000', '3/12/2000', '3/13/2000'] * 1000)
|
||
>>> s.head()
|
||
0 3/11/2000
|
||
1 3/12/2000
|
||
2 3/13/2000
|
||
3 3/11/2000
|
||
4 3/12/2000
|
||
dtype: object
|
||
|
||
>>> %timeit pd.to_datetime(s, infer_datetime_format=True) # doctest: +SKIP
|
||
100 loops, best of 3: 10.4 ms per loop
|
||
|
||
>>> %timeit pd.to_datetime(s, infer_datetime_format=False) # doctest: +SKIP
|
||
1 loop, best of 3: 471 ms per loop
|
||
|
||
Using a unix epoch time
|
||
|
||
>>> pd.to_datetime(1490195805, unit='s')
|
||
Timestamp('2017-03-22 15:16:45')
|
||
>>> pd.to_datetime(1490195805433502912, unit='ns')
|
||
Timestamp('2017-03-22 15:16:45.433502912')
|
||
|
||
.. warning:: For float arg, precision rounding might happen. To prevent
|
||
unexpected behavior use a fixed-width exact type.
|
||
|
||
Using a non-unix epoch origin
|
||
|
||
>>> pd.to_datetime([1, 2, 3], unit='D',
|
||
... origin=pd.Timestamp('1960-01-01'))
|
||
DatetimeIndex(['1960-01-02', '1960-01-03', '1960-01-04'],
|
||
dtype='datetime64[ns]', freq=None)
|
||
|
||
**Non-convertible date/times**
|
||
|
||
If a date does not meet the `timestamp limitations
|
||
<https://pandas.pydata.org/pandas-docs/stable/user_guide/timeseries.html
|
||
#timeseries-timestamp-limits>`_, passing ``errors='ignore'``
|
||
will return the original input instead of raising any exception.
|
||
|
||
Passing ``errors='coerce'`` will force an out-of-bounds date to :const:`NaT`,
|
||
in addition to forcing non-dates (or non-parseable dates) to :const:`NaT`.
|
||
|
||
>>> pd.to_datetime('13000101', format='%Y%m%d', errors='ignore')
|
||
datetime.datetime(1300, 1, 1, 0, 0)
|
||
>>> pd.to_datetime('13000101', format='%Y%m%d', errors='coerce')
|
||
NaT
|
||
|
||
.. _to_datetime_tz_examples:
|
||
|
||
**Timezones and time offsets**
|
||
|
||
The default behaviour (``utc=False``) is as follows:
|
||
|
||
- Timezone-naive inputs are converted to timezone-naive :class:`DatetimeIndex`:
|
||
|
||
>>> pd.to_datetime(['2018-10-26 12:00', '2018-10-26 13:00:15'])
|
||
DatetimeIndex(['2018-10-26 12:00:00', '2018-10-26 13:00:15'],
|
||
dtype='datetime64[ns]', freq=None)
|
||
|
||
- Timezone-aware inputs *with constant time offset* are converted to
|
||
timezone-aware :class:`DatetimeIndex`:
|
||
|
||
>>> pd.to_datetime(['2018-10-26 12:00 -0500', '2018-10-26 13:00 -0500'])
|
||
DatetimeIndex(['2018-10-26 12:00:00-05:00', '2018-10-26 13:00:00-05:00'],
|
||
dtype='datetime64[ns, pytz.FixedOffset(-300)]', freq=None)
|
||
|
||
- However, timezone-aware inputs *with mixed time offsets* (for example
|
||
issued from a timezone with daylight savings, such as Europe/Paris)
|
||
are **not successfully converted** to a :class:`DatetimeIndex`. Instead a
|
||
simple :class:`Index` containing :class:`datetime.datetime` objects is
|
||
returned:
|
||
|
||
>>> pd.to_datetime(['2020-10-25 02:00 +0200', '2020-10-25 04:00 +0100'])
|
||
Index([2020-10-25 02:00:00+02:00, 2020-10-25 04:00:00+01:00],
|
||
dtype='object')
|
||
|
||
- A mix of timezone-aware and timezone-naive inputs is converted to
|
||
a timezone-aware :class:`DatetimeIndex` if the offsets of the timezone-aware
|
||
are constant:
|
||
|
||
>>> from datetime import datetime
|
||
>>> pd.to_datetime(["2020-01-01 01:00 -01:00", datetime(2020, 1, 1, 3, 0)])
|
||
DatetimeIndex(['2020-01-01 01:00:00-01:00', '2020-01-01 02:00:00-01:00'],
|
||
dtype='datetime64[ns, pytz.FixedOffset(-60)]', freq=None)
|
||
|
||
- Finally, mixing timezone-aware strings and :class:`datetime.datetime` always
|
||
raises an error, even if the elements all have the same time offset.
|
||
|
||
>>> from datetime import datetime, timezone, timedelta
|
||
>>> d = datetime(2020, 1, 1, 18, tzinfo=timezone(-timedelta(hours=1)))
|
||
>>> pd.to_datetime(["2020-01-01 17:00 -0100", d])
|
||
Traceback (most recent call last):
|
||
...
|
||
ValueError: Tz-aware datetime.datetime cannot be converted to datetime64
|
||
unless utc=True
|
||
|
||
|
|
||
|
||
Setting ``utc=True`` solves most of the above issues:
|
||
|
||
- Timezone-naive inputs are *localized* as UTC
|
||
|
||
>>> pd.to_datetime(['2018-10-26 12:00', '2018-10-26 13:00'], utc=True)
|
||
DatetimeIndex(['2018-10-26 12:00:00+00:00', '2018-10-26 13:00:00+00:00'],
|
||
dtype='datetime64[ns, UTC]', freq=None)
|
||
|
||
- Timezone-aware inputs are *converted* to UTC (the output represents the
|
||
exact same datetime, but viewed from the UTC time offset `+00:00`).
|
||
|
||
>>> pd.to_datetime(['2018-10-26 12:00 -0530', '2018-10-26 12:00 -0500'],
|
||
... utc=True)
|
||
DatetimeIndex(['2018-10-26 17:30:00+00:00', '2018-10-26 17:00:00+00:00'],
|
||
dtype='datetime64[ns, UTC]', freq=None)
|
||
|
||
- Inputs can contain both naive and aware, string or datetime, the above
|
||
rules still apply
|
||
|
||
>>> pd.to_datetime(['2018-10-26 12:00', '2018-10-26 12:00 -0530',
|
||
... datetime(2020, 1, 1, 18),
|
||
... datetime(2020, 1, 1, 18,
|
||
... tzinfo=timezone(-timedelta(hours=1)))],
|
||
... utc=True)
|
||
DatetimeIndex(['2018-10-26 12:00:00+00:00', '2018-10-26 17:30:00+00:00',
|
||
'2020-01-01 18:00:00+00:00', '2020-01-01 19:00:00+00:00'],
|
||
dtype='datetime64[ns, UTC]', freq=None)
|
||
"""
|
||
if arg is None:
|
||
return None
|
||
|
||
if origin != "unix":
|
||
arg = _adjust_to_origin(arg, origin, unit)
|
||
|
||
tz = "utc" if utc else None
|
||
convert_listlike = partial(
|
||
_convert_listlike_datetimes,
|
||
tz=tz,
|
||
unit=unit,
|
||
dayfirst=dayfirst,
|
||
yearfirst=yearfirst,
|
||
errors=errors,
|
||
exact=exact,
|
||
infer_datetime_format=infer_datetime_format,
|
||
)
|
||
|
||
result: Timestamp | NaTType | Series | Index
|
||
|
||
if isinstance(arg, Timestamp):
|
||
result = arg
|
||
if tz is not None:
|
||
if arg.tz is not None:
|
||
result = arg.tz_convert(tz)
|
||
else:
|
||
result = arg.tz_localize(tz)
|
||
elif isinstance(arg, ABCSeries):
|
||
cache_array = _maybe_cache(arg, format, cache, convert_listlike)
|
||
if not cache_array.empty:
|
||
result = arg.map(cache_array)
|
||
else:
|
||
values = convert_listlike(arg._values, format)
|
||
result = arg._constructor(values, index=arg.index, name=arg.name)
|
||
elif isinstance(arg, (ABCDataFrame, abc.MutableMapping)):
|
||
result = _assemble_from_unit_mappings(arg, errors, tz)
|
||
elif isinstance(arg, Index):
|
||
cache_array = _maybe_cache(arg, format, cache, convert_listlike)
|
||
if not cache_array.empty:
|
||
result = _convert_and_box_cache(arg, cache_array, name=arg.name)
|
||
else:
|
||
result = convert_listlike(arg, format, name=arg.name)
|
||
elif is_list_like(arg):
|
||
try:
|
||
cache_array = _maybe_cache(arg, format, cache, convert_listlike)
|
||
except OutOfBoundsDatetime:
|
||
# caching attempts to create a DatetimeIndex, which may raise
|
||
# an OOB. If that's the desired behavior, then just reraise...
|
||
if errors == "raise":
|
||
raise
|
||
# ... otherwise, continue without the cache.
|
||
from pandas import Series
|
||
|
||
cache_array = Series([], dtype=object) # just an empty array
|
||
if not cache_array.empty:
|
||
result = _convert_and_box_cache(arg, cache_array)
|
||
else:
|
||
result = convert_listlike(arg, format)
|
||
else:
|
||
result = convert_listlike(np.array([arg]), format)[0]
|
||
|
||
# error: Incompatible return value type (got "Union[Timestamp, NaTType,
|
||
# Series, Index]", expected "Union[DatetimeIndex, Series, float, str,
|
||
# NaTType, None]")
|
||
return result # type: ignore[return-value]
|
||
|
||
|
||
# mappings for assembling units
|
||
_unit_map = {
|
||
"year": "year",
|
||
"years": "year",
|
||
"month": "month",
|
||
"months": "month",
|
||
"day": "day",
|
||
"days": "day",
|
||
"hour": "h",
|
||
"hours": "h",
|
||
"minute": "m",
|
||
"minutes": "m",
|
||
"second": "s",
|
||
"seconds": "s",
|
||
"ms": "ms",
|
||
"millisecond": "ms",
|
||
"milliseconds": "ms",
|
||
"us": "us",
|
||
"microsecond": "us",
|
||
"microseconds": "us",
|
||
"ns": "ns",
|
||
"nanosecond": "ns",
|
||
"nanoseconds": "ns",
|
||
}
|
||
|
||
|
||
def _assemble_from_unit_mappings(arg, errors, tz):
|
||
"""
|
||
assemble the unit specified fields from the arg (DataFrame)
|
||
Return a Series for actual parsing
|
||
|
||
Parameters
|
||
----------
|
||
arg : DataFrame
|
||
errors : {'ignore', 'raise', 'coerce'}, default 'raise'
|
||
|
||
- If :const:`'raise'`, then invalid parsing will raise an exception
|
||
- If :const:`'coerce'`, then invalid parsing will be set as :const:`NaT`
|
||
- If :const:`'ignore'`, then invalid parsing will return the input
|
||
tz : None or 'utc'
|
||
|
||
Returns
|
||
-------
|
||
Series
|
||
"""
|
||
from pandas import (
|
||
DataFrame,
|
||
to_numeric,
|
||
to_timedelta,
|
||
)
|
||
|
||
arg = DataFrame(arg)
|
||
if not arg.columns.is_unique:
|
||
raise ValueError("cannot assemble with duplicate keys")
|
||
|
||
# replace passed unit with _unit_map
|
||
def f(value):
|
||
if value in _unit_map:
|
||
return _unit_map[value]
|
||
|
||
# m is case significant
|
||
if value.lower() in _unit_map:
|
||
return _unit_map[value.lower()]
|
||
|
||
return value
|
||
|
||
unit = {k: f(k) for k in arg.keys()}
|
||
unit_rev = {v: k for k, v in unit.items()}
|
||
|
||
# we require at least Ymd
|
||
required = ["year", "month", "day"]
|
||
req = sorted(set(required) - set(unit_rev.keys()))
|
||
if len(req):
|
||
_required = ",".join(req)
|
||
raise ValueError(
|
||
"to assemble mappings requires at least that "
|
||
f"[year, month, day] be specified: [{_required}] is missing"
|
||
)
|
||
|
||
# keys we don't recognize
|
||
excess = sorted(set(unit_rev.keys()) - set(_unit_map.values()))
|
||
if len(excess):
|
||
_excess = ",".join(excess)
|
||
raise ValueError(
|
||
f"extra keys have been passed to the datetime assemblage: [{_excess}]"
|
||
)
|
||
|
||
def coerce(values):
|
||
# we allow coercion to if errors allows
|
||
values = to_numeric(values, errors=errors)
|
||
|
||
# prevent overflow in case of int8 or int16
|
||
if is_integer_dtype(values):
|
||
values = values.astype("int64", copy=False)
|
||
return values
|
||
|
||
values = (
|
||
coerce(arg[unit_rev["year"]]) * 10000
|
||
+ coerce(arg[unit_rev["month"]]) * 100
|
||
+ coerce(arg[unit_rev["day"]])
|
||
)
|
||
try:
|
||
values = to_datetime(values, format="%Y%m%d", errors=errors, utc=tz)
|
||
except (TypeError, ValueError) as err:
|
||
raise ValueError(f"cannot assemble the datetimes: {err}") from err
|
||
|
||
for u in ["h", "m", "s", "ms", "us", "ns"]:
|
||
value = unit_rev.get(u)
|
||
if value is not None and value in arg:
|
||
try:
|
||
values += to_timedelta(coerce(arg[value]), unit=u, errors=errors)
|
||
except (TypeError, ValueError) as err:
|
||
raise ValueError(
|
||
f"cannot assemble the datetimes [{value}]: {err}"
|
||
) from err
|
||
return values
|
||
|
||
|
||
def _attempt_YYYYMMDD(arg: np.ndarray, errors: str) -> np.ndarray | None:
|
||
"""
|
||
try to parse the YYYYMMDD/%Y%m%d format, try to deal with NaT-like,
|
||
arg is a passed in as an object dtype, but could really be ints/strings
|
||
with nan-like/or floats (e.g. with nan)
|
||
|
||
Parameters
|
||
----------
|
||
arg : np.ndarray[object]
|
||
errors : {'raise','ignore','coerce'}
|
||
"""
|
||
|
||
def calc(carg):
|
||
# calculate the actual result
|
||
carg = carg.astype(object)
|
||
parsed = parsing.try_parse_year_month_day(
|
||
carg / 10000, carg / 100 % 100, carg % 100
|
||
)
|
||
return tslib.array_to_datetime(parsed, errors=errors)[0]
|
||
|
||
def calc_with_mask(carg, mask):
|
||
result = np.empty(carg.shape, dtype="M8[ns]")
|
||
iresult = result.view("i8")
|
||
iresult[~mask] = iNaT
|
||
|
||
masked_result = calc(carg[mask].astype(np.float64).astype(np.int64))
|
||
result[mask] = masked_result.astype("M8[ns]")
|
||
return result
|
||
|
||
# try intlike / strings that are ints
|
||
try:
|
||
return calc(arg.astype(np.int64))
|
||
except (ValueError, OverflowError, TypeError):
|
||
pass
|
||
|
||
# a float with actual np.nan
|
||
try:
|
||
carg = arg.astype(np.float64)
|
||
return calc_with_mask(carg, notna(carg))
|
||
except (ValueError, OverflowError, TypeError):
|
||
pass
|
||
|
||
# string with NaN-like
|
||
try:
|
||
# error: Argument 2 to "isin" has incompatible type "List[Any]"; expected
|
||
# "Union[Union[ExtensionArray, ndarray], Index, Series]"
|
||
mask = ~algorithms.isin(arg, list(nat_strings)) # type: ignore[arg-type]
|
||
return calc_with_mask(arg, mask)
|
||
except (ValueError, OverflowError, TypeError):
|
||
pass
|
||
|
||
return None
|
||
|
||
|
||
def to_time(arg, format=None, infer_time_format=False, errors="raise"):
|
||
# GH#34145
|
||
warnings.warn(
|
||
"`to_time` has been moved, should be imported from pandas.core.tools.times. "
|
||
"This alias will be removed in a future version.",
|
||
FutureWarning,
|
||
stacklevel=find_stack_level(),
|
||
)
|
||
from pandas.core.tools.times import to_time
|
||
|
||
return to_time(arg, format, infer_time_format, errors)
|