Class DatetimeMethods (1.0.0)

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

Returns numpy array of Python datetime.date objects.

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

The day of the datetime.

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.

The hours of the datetime.

The minutes of the datetime.

The month as January=1, December=12.

The quarter of the date.

The seconds of the datetime.

Returns numpy array of datetime.time objects.

The time part of the Timestamps.

Return the timezone.

Returns the unit of time precision.

The year of the datetime.

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]`

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]

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