Class DatetimeMethods (1.0.0)

DatetimeMethods(
    data=None,
    index: vendored_pandas_typing.Axes | None = None,
    dtype: typing.Optional[
        bigframes.dtypes.DtypeString | bigframes.dtypes.Dtype
    ] = None,
    name: str | None = None,
    copy: typing.Optional[bool] = None,
    *,
    session: typing.Optional[bigframes.session.Session] = None
)

Accessor object for datetime-like properties of the Series values.

Properties

date

Returns numpy array of Python datetime.date objects.

Namely, the date part of Timestamps without time and timezone information.

day

The day of the datetime.

dayofweek

The day of the week with Monday=0, Sunday=6.

Return the day of the week. It is assumed the week starts on Monday, which is denoted by 0 and ends on Sunday which is denoted by 6. This method is available on both Series with datetime values (using the dt accessor) or DatetimeIndex.

Returns
Type	Description
`Series or Index`	Containing integers indicating the day number.

hour

The hours of the datetime.

minute

The minutes of the datetime.

month

The month as January=1, December=12.

quarter

The quarter of the date.

second

The seconds of the datetime.

time

Returns numpy array of datetime.time objects.

The time part of the Timestamps.

tz

Return the timezone.

unit

Returns the unit of time precision.

year

The year of the datetime.

Methods

floor

floor(freq: str) -> bigframes.series.Series

Perform floor operation on the data to the specified freq.

Supported freq arguments are: 'Y' (year), 'Q' (quarter), 'M' (month), 'W' (week), 'D' (day), 'h' (hour), 'min' (minute), 's' (second), 'ms' (microsecond), 'us' (nanosecond), 'ns' (nanosecond)

Behavior around clock changes (i.e. daylight savings) is determined by the SQL engine, so "ambiguous" and "nonexistent" parameters are not supported. Y, Q, M, and W freqs are not supported by pandas as of version 2.2, but have been added here due to backend support.

Examples:

>>> import pandas as pd
>>> import bigframes.pandas as bpd
>>> rng = pd.date_range('1/1/2018 11:59:00', periods=3, freq='min')
>>> bpd.Series(rng).dt.floor("h")
0    2018-01-01 11:00:00
1    2018-01-01 12:00:00
2    2018-01-01 12:00:00
dtype: timestamp`us][pyarrow]`

Parameter
Name	Description
`freq`	`str` Frequency string (e.g. "D", "min", "s").

normalize

normalize() -> bigframes.series.Series

Convert times to midnight.

The time component of the date-time is converted to midnight i.e. 00:00:00. This is useful in cases when the time does not matter. The return dtype will match the source series.

This method is available on Series with datetime values under the .dt accessor.

Examples:

>>> import pandas as pd
>>> import bigframes.pandas as bpd
>>> s = bpd.Series(pd.date_range(
...     start='2014-08-01 10:00',
...     freq='h',
...     periods=3,
...     tz='Asia/Calcutta')) # note timezones will be converted to UTC here
>>> s.dt.normalize()
0    2014-08-01 00:00:00+00:00
1    2014-08-01 00:00:00+00:00
2    2014-08-01 00:00:00+00:00
dtype: timestamp[us, tz=UTC][pyarrow]

strftime

strftime(date_format: str) -> bigframes.series.Series

Convert to string Series using specified date_format.

Return a Series of formatted strings specified by date_format. Details of the string format can be found in BigQuery format elements doc <%(https://cloud.google.com/bigquery/docs/reference/standard-sql/format-elements)s>__.

Examples:

>>> import bigframes.pandas as bpd
>>> bpd.options.display.progress_bar = None

>>> s = bpd.to_datetime(
...     ['2014-08-15 08:15:12', '2012-02-29 08:15:12+06:00', '2015-08-15 08:15:12+05:00'],
...     utc=True
... ).astype("timestamp[us, tz=UTC][pyarrow]")

>>> s.dt.strftime("%B %d, %Y, %r")
0      August 15, 2014, 08:15:12 AM
1    February 29, 2012, 02:15:12 AM
2      August 15, 2015, 03:15:12 AM
Name: 0, dtype: string

Parameter
Name	Description
`date_format`	`str` Date format string (e.g. "%Y-%m-%d").

Returns
Type	Description
`bigframes.series.Series`	Series of formatted strings.