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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. |