10 examples of 'object to datetime pandas' in Python

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def to_pandas(self) -> None:
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    '''Return a Pandas Index.
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    '''
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    raise NotImplementedError('Pandas does not support a year month type, and it is ambiguous if a date proxy should be the first of the month or the last of the month.')

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def to_datetime(self, dayfirst=False):
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    """
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    DEPRECATED: use :meth:`to_timestamp` instead.
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    Cast to DatetimeIndex.
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    """
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    warnings.warn("to_datetime is deprecated. Use self.to_timestamp(...)",
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                  FutureWarning, stacklevel=2)
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    return self.to_timestamp()

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def localize_datetime(x):
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    if pd.isnull(x) or isinstance(x, pd.tslib.NaTType):
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        return None
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    utz_tz = pytz.timezone('UTC')
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    local_tz = pytz.timezone(utc_to_tz)
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    d = utz_tz.localize(x)
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    d = d.astimezone(local_tz)
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    # make naive.
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    return d.replace(tzinfo=None)

30
def converter(df: DataFrame) -> Series:
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    return df[field_name]

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def _is_datetime(s):
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    if is_datetime64_any_dtype(s):
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        return True
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    try:
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        if is_object_dtype(s):
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            pd.to_datetime(s, infer_datetime_format=True)
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            return True
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    except Exception:  # pylint: disable=broad-except
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        pass
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    return False

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def to_datetime(obj):
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    """Convert `obj` into a `datetime` object.
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    * If `obj` is a `datetime` object it is returned untouched.
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    * if `obj` is None the current time in UTC is used.
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    * if `obj` is a `date` object it will be converted into a `datetime` object.
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    * if `obj` is a number it is interpreted as a timestamp.
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    Note: All values are localized to the users timezone.
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          Never use this for calculations, only for presentation!
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    """
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    if obj is None:
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        dt = datetime.utcnow()
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    elif isinstance(obj, datetime):
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        dt = obj
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    elif isinstance(obj, date):
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        dt = datetime(obj.year, obj.month, obj.day)
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    elif isinstance(obj, (int, long, float)):
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        dt = datetime.fromtimestamp(obj)
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    else:
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        raise TypeError('expecting datetime, date int, long, float, or None; '
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                        'got %s' % type(obj))
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    return rebase_to_timezone(dt)

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@classmethod
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def to_datetime(cls, obj, end_of_year=False):
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    """Create datetime object from the inputted object."""
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    if isinstance(obj, bool):
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        return obj
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    if isinstance(obj, datetime.datetime):
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        return obj
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    if isinstance(obj, int):
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        if end_of_year:
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            month, day = 12, 31
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        else:
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            month, day = 1, 1
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        return datetime.datetime(obj, month, day)
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    if isinstance(obj, str):
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        return cls.str_to_datetime(obj)
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    try:
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        return datetime.datetime(obj)
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    except:
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        raise

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def as_timestamp(obj):
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    if isinstance(obj, Timestamp):
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        return obj
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    try:
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        return Timestamp(obj)
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    except (OutOfBoundsDatetime):
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        pass
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    return obj

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def datesarray_to_datetimearray(dates: np.ndarray) -> np.ndarray:
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    """
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    Convert an pandas-array of timestamps into
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    An numpy-array of datetimes
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    :return: numpy-array of datetime
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    """
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    return dates.dt.to_pydatetime()

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def to_timestamp(self, freq=None, how='start'):
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    """
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    Cast to DatetimeArray/Index.
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    Parameters
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    ----------
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    freq : string or DateOffset, optional
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        Target frequency. The default is 'D' for week or longer,
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        'S' otherwise
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    how : {'s', 'e', 'start', 'end'}
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    Returns
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    -------
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    DatetimeArray/Index
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    """
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    from pandas.core.arrays import DatetimeArray
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    how = libperiod._validate_end_alias(how)
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    end = how == 'E'
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    if end:
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        if freq == 'B':
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            # roll forward to ensure we land on B date
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            adjust = Timedelta(1, 'D') - Timedelta(1, 'ns')
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            return self.to_timestamp(how='start') + adjust
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        else:
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            adjust = Timedelta(1, 'ns')
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            return (self + self.freq).to_timestamp(how='start') - adjust
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    if freq is None:
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        base, mult = libfrequencies.get_freq_code(self.freq)
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        freq = libfrequencies.get_to_timestamp_base(base)
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    else:
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        freq = Period._maybe_convert_freq(freq)
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    base, mult = libfrequencies.get_freq_code(freq)
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    new_data = self.asfreq(freq, how=how)
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    new_data = libperiod.periodarr_to_dt64arr(new_data.asi8, base)
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    return DatetimeArray._from_sequence(new_data, freq='infer')