# Copyright 1999-2020 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_scalar, is_dict_like from ... import opcodes from ...serialize import KeyField, StringField, BoolField, AnyField from ...tensor import tensor as astensor from ...tensor.core import TENSOR_CHUNK_TYPE from ...tiles import TilesError from ..core import DATAFRAME_TYPE, SERIES_TYPE, INDEX_TYPE, INDEX_CHUNK_TYPE from ..initializer import DataFrame as asdataframe, Series as asseries, Index as asindex from ..operands import DataFrameOperand, DataFrameOperandMixin from ..utils import parse_index class DataFrameToDatetime(DataFrameOperand, DataFrameOperandMixin): _op_type_ = opcodes.TO_DATETIME _arg = KeyField('arg') _errors = StringField('errors') _dayfirst = BoolField('dayfirst') _yearfirst = BoolField('yearfirst') _utc = BoolField('utc') _format = StringField('format') _exact = BoolField('exact') _unit = StringField('unit') _infer_datetime_format = BoolField('infer_datetime_format') _origin = AnyField('origin') _cache = BoolField('cache') def __init__(self, errors=None, dayfirst=None, yearfirst=None, utc=None, format=None, exact=None, unit=None, infer_datetime_format=None, origin=None, cache=None, **kw): super().__init__(_errors=errors, _dayfirst=dayfirst, _yearfirst=yearfirst, _utc=utc, _format=format, _exact=exact, _unit=unit, _infer_datetime_format=infer_datetime_format, _origin=origin, _cache=cache, **kw) @property def arg(self): return self._arg @property def errors(self): return self._errors @property def dayfirst(self): return self._dayfirst @property def yearfirst(self): return self._yearfirst @property def utc(self): return self._utc @property def format(self): return self._format @property def exact(self): return self._exact @property def unit(self): return self._unit @property def infer_datetime_format(self): return self._infer_datetime_format @property def origin(self): return self._origin @property def cache(self): return self._cache @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, infer_datetime_format=self._infer_datetime_format, 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 raise TilesError('unknown chunk shape on columns axis') arg = arg.rechunk({1: arg.shape[1]})._inplace_tile() 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] call = partial(pd.to_datetime, errors=op.errors, dayfirst=op.dayfirst, yearfirst=op.yearfirst, utc=op.utc, format=op.format, exact=op.exact, unit=op.unit, infer_datetime_format=op.infer_datetime_format, 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, infer_datetime_format: bool = False, 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. infer_datetime_format : bool, default False If True and no `format` is given, attempt to infer the format of the datetime strings, 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 '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 Passing infer_datetime_format=True can often-times speedup a parsing if its not an ISO8601 format exactly, but in a regular format. >>> 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, infer_datetime_format=infer_datetime_format, origin=origin, cache=cache) return op(arg)