# Copyright 2022-2023 XProbe Inc.
# derived from copyright 1999-2021 Alibaba Group Holding Ltd.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
from functools import partial
from typing import Any
import numpy as np
import pandas as pd
from pandas.api.types import is_dict_like, is_scalar
from ... import opcodes
from ...core import recursive_tile
from ...serialization.serializables import AnyField, BoolField, KeyField, StringField
from ...tensor import tensor as astensor
from ...tensor.core import TENSOR_CHUNK_TYPE
from ..core import DATAFRAME_TYPE, INDEX_CHUNK_TYPE, INDEX_TYPE, SERIES_TYPE
from ..initializer import DataFrame as asdataframe
from ..initializer import Index as asindex
from ..initializer import Series as asseries
from ..operands import DataFrameOperand, DataFrameOperandMixin
from ..utils import lazy_import, parse_index
cudf = lazy_import("cudf")
class DataFrameToDatetime(DataFrameOperand, DataFrameOperandMixin):
_op_type_ = opcodes.TO_DATETIME
arg = KeyField("arg")
errors = StringField("errors", default=None)
dayfirst = BoolField("dayfirst", default=None)
yearfirst = BoolField("yearfirst", default=None)
utc = BoolField("utc", default=None)
format = StringField("format", default=None)
exact = BoolField("exact", default=None)
unit = StringField("unit", default=None)
origin = AnyField("origin", default=None)
cache = BoolField("cache", default=None)
@property
def _params(self):
return tuple(
getattr(self, k)
for k in self._keys_
if k not in self._no_copy_attrs_ and k != "arg" and hasattr(self, k)
)
def _set_inputs(self, inputs):
super()._set_inputs(inputs)
self.arg = self._inputs[0]
def __call__(self, arg):
if is_scalar(arg):
ret = pd.to_datetime(
arg,
errors=self.errors,
dayfirst=self.dayfirst,
yearfirst=self.yearfirst,
utc=self.utc,
format=self.format,
exact=self.exact,
unit=self.unit,
origin=self.origin,
cache=self.cache,
)
return astensor(ret)
dtype = np.datetime64(1, "ns").dtype
if isinstance(arg, (pd.Series, SERIES_TYPE)):
arg = asseries(arg)
return self.new_series(
[arg],
shape=arg.shape,
dtype=dtype,
index_value=arg.index_value,
name=arg.name,
)
if is_dict_like(arg) or isinstance(arg, DATAFRAME_TYPE):
arg = asdataframe(arg)
columns = arg.columns_value.to_pandas().tolist()
if sorted(columns) != sorted(["year", "month", "day"]):
missing = ",".join(
c for c in ["day", "month", "year"] if c not in columns
)
raise ValueError(
"to assemble mappings requires at least "
f"that [year, month, day] be specified: [{missing}] is missing"
)
return self.new_series(
[arg], shape=(arg.shape[0],), dtype=dtype, index_value=arg.index_value
)
elif isinstance(arg, (pd.Index, INDEX_TYPE)):
arg = asindex(arg)
return self.new_index(
[arg],
shape=arg.shape,
dtype=dtype,
index_value=parse_index(pd.Index([], dtype=dtype), self._params, arg),
name=arg.name,
)
else:
arg = astensor(arg)
if arg.ndim != 1:
raise TypeError(
"arg must be a string, datetime, "
"list, tuple, 1-d tensor, or Series"
)
return self.new_index(
[arg],
shape=arg.shape,
dtype=dtype,
index_value=parse_index(pd.Index([], dtype=dtype), self._params, arg),
)
@classmethod
def tile(cls, op: "DataFrameToDatetime"):
out = op.outputs[0]
arg = op.arg
if isinstance(arg, DATAFRAME_TYPE):
if np.isnan(arg.shape[0]) or any(
np.isnan(s) for s in arg.nsplits[1]
): # pragma: no cover
yield
arg = yield from recursive_tile(arg.rechunk({1: arg.shape[1]}))
out_chunks = []
for chunk in arg.chunks:
chunk_op = op.copy().reset_key()
if isinstance(chunk, (TENSOR_CHUNK_TYPE, INDEX_CHUNK_TYPE)):
chunk_index_value = parse_index(
pd.Index([], dtype=out.dtype), op._params, chunk
)
else:
chunk_index_value = chunk.index_value
out_chunk = chunk_op.new_chunk(
[chunk],
shape=(chunk.shape[0],),
dtype=out.dtype,
index_value=chunk_index_value,
name=out.name,
index=(chunk.index[0],),
)
out_chunks.append(out_chunk)
params = out.params
params["nsplits"] = (arg.nsplits[0],)
params["chunks"] = out_chunks
new_op = op.copy()
return new_op.new_tileables(op.inputs, kws=[params])
@classmethod
def execute(cls, ctx, op: "DataFrameToDatetime"):
arg = ctx[op.arg.key]
unit = op.unit
if cudf and op.gpu:
func = cudf.to_datetime
if unit is None:
unit = "ns"
else:
func = pd.to_datetime
call = partial(
func,
errors=op.errors,
dayfirst=op.dayfirst,
yearfirst=op.yearfirst,
utc=op.utc,
format=op.format,
exact=op.exact,
unit=unit,
origin=op.origin,
cache=op.cache,
)
try:
ctx[op.outputs[0].key] = call(arg)
except ValueError: # pragma: no cover
ctx[op.outputs[0].key] = call(arg.copy())
[docs]def to_datetime(
arg,
errors: str = "raise",
dayfirst: bool = False,
yearfirst: bool = False,
utc: bool = None,
format: str = None,
exact: bool = True,
unit: str = None,
origin: Any = "unix",
cache: bool = True,
):
"""
Convert argument to datetime.
Parameters
----------
arg : int, float, str, datetime, list, tuple, 1-d array, Series DataFrame/dict-like
The object to convert to a datetime.
errors : {'ignore', 'raise', 'coerce'}, default 'raise'
- If 'raise', then invalid parsing will raise an exception.
- If 'coerce', then invalid parsing will be set as NaT.
- If 'ignore', then invalid parsing will return the input.
dayfirst : bool, default False
Specify a date parse order if `arg` is str or its list-likes.
If True, parses dates with the day first, eg 10/11/12 is parsed as
2012-11-10.
Warning: dayfirst=True is not strict, but will prefer to parse
with day first (this is a known bug, based on dateutil behavior).
yearfirst : bool, default False
Specify a date parse order if `arg` is str or its list-likes.
- If True parses dates with the year first, eg 10/11/12 is parsed as
2010-11-12.
- If both dayfirst and yearfirst are True, yearfirst is preceded (same
as dateutil).
Warning: yearfirst=True is not strict, but will prefer to parse
with year first (this is a known bug, based on dateutil behavior).
utc : bool, default None
Return UTC DatetimeIndex if True (converting any tz-aware
datetime.datetime objects as well).
format : str, default None
The strftime to parse time, eg "%d/%m/%Y", note that "%f" will parse
all the way up to nanoseconds.
See strftime documentation for more information on choices:
https://docs.python.org/3/library/datetime.html#strftime-and-strptime-behavior.
exact : bool, True by default
Behaves as:
- If True, require an exact format match.
- If 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.
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 'unix' (or POSIX) time; origin is set to 1970-01-01.
- If 'julian', unit must be 'D', and origin is set to beginning of
Julian Calendar. Julian day number 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 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.
Returns
-------
datetime
If parsing succeeded.
Return type depends on input:
- list-like: DatetimeIndex
- Series: Series of datetime64 dtype
- scalar: Timestamp
In case when it is not possible to return designated types (e.g. when
any element of input is before Timestamp.min or after Timestamp.max)
return will have datetime.datetime type (or corresponding
array/Series).
See Also
--------
DataFrame.astype : Cast argument to a specified dtype.
to_timedelta : Convert argument to timedelta.
convert_dtypes : Convert dtypes.
Examples
--------
Assembling a datetime from multiple columns of a DataFrame. The keys can be
common abbreviations like ['year', 'month', 'day', 'minute', 'second',
'ms', 'us', 'ns']) or plurals of the same
>>> import mars.dataframe as md
>>> df = md.DataFrame({'year': [2015, 2016],
... 'month': [2, 3],
... 'day': [4, 5]})
>>> md.to_datetime(df).execute()
0 2015-02-04
1 2016-03-05
dtype: datetime64[ns]
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 NaT,
in addition to forcing non-dates (or non-parseable dates) to NaT.
>>> md.to_datetime('13000101', format='%Y%m%d', errors='ignore').execute()
datetime.datetime(1300, 1, 1, 0, 0)
>>> md.to_datetime('13000101', format='%Y%m%d', errors='coerce').execute()
NaT
>>> s = md.Series(['3/11/2000', '3/12/2000', '3/13/2000'] * 1000)
>>> s.head().execute()
0 3/11/2000
1 3/12/2000
2 3/13/2000
3 3/11/2000
4 3/12/2000
dtype: object
Using a unix epoch time
>>> md.to_datetime(1490195805, unit='s').execute()
Timestamp('2017-03-22 15:16:45')
>>> md.to_datetime(1490195805433502912, unit='ns').execute()
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
>>> md.to_datetime([1, 2, 3], unit='D',
... origin=md.Timestamp('1960-01-01')).execute()
DatetimeIndex(['1960-01-02', '1960-01-03', '1960-01-04'], \
dtype='datetime64[ns]', freq=None)
"""
op = DataFrameToDatetime(
errors=errors,
dayfirst=dayfirst,
yearfirst=yearfirst,
utc=utc,
format=format,
exact=exact,
unit=unit,
origin=origin,
cache=cache,
)
return op(arg)