Datetime properties via `.dt` in Pandas.

When you have a datetime column, then you're able to parse lots of information from it. The trick is to refer to the .dt accessor that's attached to the column.

Using dt

Here's a few examples.

import pandas as pd

df = pd.read_csv("https://calmcode.io/datasets/birthdays.csv")

(df
.assign(date=lambda d: pd.to_datetime(d['date'], format="%Y-%m-%d"),
        day_of_week=lambda d: d['date'].dt.day_name(),
        minute=lambda d: d['date'].dt.minute,
        nanosecond=lambda d: d['date'].dt.nanosecond,
        day_of_year=lambda d: d['date'].dt.day_of_year,
        month_manual=lambda d: d['date'].dt.month,
        week=lambda d: d['date'].dt.isocalendar().week))

Pay close attention to the week property. Since pandas 1.10 it's recommended to use d['date'].dt.isocalendar().week instead of d['date'].dt.week.

More dt properties.

There's are lot's of properties that you can access this way. Here's a list of most properties you may want to use.

Property	Description
year	The year of the datetime
month	The month of the datetime
day	The days of the datetime
hour	The hour of the datetime
minute	The minutes of the datetime
second	The seconds of the datetime
microsecond	The microseconds of the datetime
nanosecond	The nanoseconds of the datetime
dayofyear	The ordinal day of year
weekday	The number of the day of the week with Monday=0, Sunday=6
quarter	Quarter of the date: Jan-Mar = 1, Apr-Jun = 2, etc.
days_in_month	The number of days in the month of the datetime
is_leap_year	Logical indicating if the date belongs to a leap year

This is a subset of all the properties listed on the pandas documentation. For a full overview of all .dt properties, do check the full table on their docs.