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All data will be automatically allocated with one at the beginning “DOB”（ Date of birth ）. therefore , Date and time data will inevitably be encountered when processing data at some time . This tutorial will take you through Python Medium datetime Module and use some peripheral libraries , Such as pandas and pytz.

stay Python in , Anything related to date and time is done by datetime Module processing , It further divides the modules into 5 Different classes . A class is simply a data type corresponding to an object . The figure below summarizes Python Medium 5 Date time classes, as well as common properties and examples .

3 A useful fragment

1、 Convert string to date time format , Maybe it's using datetime The most common situation .

Due to the alphanumeric nature of the date and time , Parse similar dates and times into Python It is usually interpreted as a string . In this section , We will show you how to parse a string list into a datetime format , And how to split and combine date and time data into columns in the data frame .

fragment 1 The printout of

however , What if the date time is formatted in an unusual or ambiguous way ？ A common problem is the difference between the date and time writing methods in the United States and Europe . In American format , Month before , And in the European style , The date is before .

By default ,pandas Medium to_datetime By reducing the previous one to 12 position (<12) To convert an object to a date time... By parsing the number of . for example ,2/11/2021 Will be resolved to 2021-02-11, but 13/11/2021 Will be resolved to 2021-11-13. however , Having this format parameter allows you to define the format in other ways .

perhaps , strftime() Method helps to format the datetime before returning a string . In the following example , Dash between original date and time (-) Replace with backslash (/), Digital month (02) Replace with abbreviated English terms (Feb).

Because there are many ways to interpret dates （ Japan 、 month 、 year ） And time （ when 、 branch 、 second ）, So it's important to understand the different format codes . The following table is a memo of common format codes .

2、 Use time zones

No time zone information datetime Objects are called “naive”, The object of the zone information （ Usually with at the end +HH:MM Corresponding GMT） Is considered to be “aware”.pytz May be Python One of the most comprehensive libraries in , It simplifies the task of time zone calculation .

The following code snippet will show you how to “naive” and “aware” datetime Convert between objects , And you can use different time zones . The last part of the code also demonstrates how to convert a given datetime object to a local time zone . This example shows the time zone codes for Japan and Germany , For other regions , You can refer to here .

fragment 2 Output

3、 Use interval Compare the two datetime(s)

Sometimes we have to compare two dates and times conditionally . Imagine , You have two data frames , The first data frame contains only one column of date and time , The second data frame contains two columns representing the interval and other information in the remaining columns . Your goal is to find a matching date and time from the first data frame , If it falls within the interval of the second data frame , If it is , Copy other columns .

One way to do this is to use pd.Interval Compress the interval between two dates , They are then assigned as indexes to the data frame , It can be used later to conditionally compare and map dates and times . If the time condition is met , This can be done by using for Loop through the columns of interest to complete .

original text ：https://towardsdatascience.com/how-to-work-with-datetime-in-python-26d4092dc484

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