10 examples of 'convert pandas series to datetime' in Python

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def get_is_daytime_from_series(series: pd.Series) -> pd.Series:
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    """Return if time in daytime interval."""
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    interval = DAY_INTERVAL
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    return get_time_is_in_interval_from_series(series,
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                                               start_time=interval[0],
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                                               end_time=interval[1])

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

553
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 _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_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')

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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)

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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.')

84
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

8
def convert_to_datetime(datetime_str):
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  tweet_datetime = datetime.strptime(datetime_str,'%a %b %d %H:%M:%S %z %Y')
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  return(tweet_datetime)

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def datetime_to_seconds(arr):
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    """Convert an array of datetime64[ns] to seconds since the UNIX epoch"""
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    if arr.dtype != np.dtype('datetime64[ns]'):
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        if arr.dtype == 'int64':
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            # The user has passed a unix timestamp already
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            return arr
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        elif arr.dtype == 'object' or str(arr.dtype).startswith(
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                                                        'datetime64[ns,'):
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            # Convert to datetime64[ns] from string
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            # Or from datetime with timezone information
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            # Return timestamp in 'UTC'
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            arr = pd.to_datetime(arr, utc=True)
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        else:
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            raise TypeError(f"Invalid dtype '{arr.dtype}', expected one of: "
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                            "datetime64[ns], int64 (UNIX epoch), "
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                            "or object (string)")
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    return arr.view('i8') // 10**9  # ns -> s since epoch