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This article will explain in detail how pandas uses dt objects. The editor thinks it is very practical, so I share it with you as a reference. I hope you can get something after reading this article.

The use of dt objects

Series data type: datetime

Because the data requires a datetime type, the following uses pandas's date_range () to generate a set of dates datetime that demonstrates how to manipulate dt objects.

> daterng = pd.Series (pd.date_range ('2017, periods=9) Freq='Q') > > daterng0 2017-03-311 2017-06-2017-09-303 2017-12-314 2018-03-315 2018-06-306 2018-09-307 2018-12-318 2019-03-31dtype: datetime64 [ns] > > daterng.dt.day_name () 0 Friday1 Friday2 Saturday3 Sunday4 Saturday5 Saturday6 Sunday7 Monday8 Sundaydtype: object > > # check the second half > > daterng [daterng.dt.quarter > 2] 2 2017-09-303 2017-12-316 2018-09-2018-12-31dtype: datetime64 [ns] > > daterng [daterng.dt.is _ year_end] 3 2017-12-317 2018-12-31dtype: datetime64 [ns]

The above three methods for dt are explained:

Series.dt.day_name (): judge the week from the date

Series.dt.quarter: judge the season by the date

Series.dt.is_year_end: judging from the date whether it is at the end of the year

Other methods are also based on some transformations of datetime, and use the transformations to see specific micro or macro dates.

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