As of January 1, 2020 this library no longer supports Python 2 on the latest released version. Library versions released prior to that date will continue to be available. For more information please visit Python 2 support on Google Cloud.

Using the db-dtypes package

Importing the db_dtypes module registers the extension dtypes for use in pandas.

Construct a date Series with strings in YYYY-MM-DD format or datetime.date objects.

import datetime

import pandas as pd

import db_dtypes  # noqa import to register dtypes

dates = pd.Series([datetime.date(2021, 9, 17), "2021-9-18"], dtype="dbdate")

Working with dates

Convert a date Series to a datetime64 Series with astype(). The resulting values use midnight as the time part.

datetimes = dates.astype("datetime64")

Just like datetime64 values, date values can be subtracted. This is equivalent to first converting to datetime64 and then subtracting.

dates2 = pd.Series(["2021-1-1", "2021-1-2"], dtype="dbdate")
diffs = dates - dates2

Just like datetime64 values, DateOffset values can be added to them.

do = pd.DateOffset(days=1)
after = dates + do
before = dates - do

Working with times

Construct a time Series with strings in HH:MM:SS.fraction 24-hour format or datetime.time objects.

times = pd.Series([datetime.time(1, 2, 3, 456789), "12:00:00.6"], dtype="dbtime")

Convert a time Series to a timedelta64 Series with astype().

timedeltas = times.astype("timedelta64")

Combining dates and times

Combine a date Series with a time Series to create a datetime64 Series.

combined = dates + times