How to use DateTime in Python

All data is automatically assigned a "DOB" (Date of Birth) at the beginning. Therefore, it is inevitable to encounter date and time data when processing data at some point. This tutorial will take you through the datetime module in Python and using some peripheral libraries such as pandas and pytz.

In Python, anything related to date and time is handled by the datetime module, which further divides the module into 5 different classes. Classes are simply data types that correspond to objects. The figure below summarizes the 5 datetime classes in Python along with commonly used properties and examples.

#3 useful snippets

1. Convert a string to datetime format, perhaps the most common case of using datetime.

Due to the alphanumeric nature of dates and times, parsing similar dates and times into Python will often be interpreted as strings. In this section, we'll cover how to parse a list of strings into datetime format, and how to split and combine date and time data into individual columns in a data frame.

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However, if the date-time behaves in an unusual or ambiguous way What about formatting? A common question is the difference between the way dates and times are written in the United States and Europe. In American format, the month comes first, while in European style, the day comes first.

By default, to_datetime in pandas converts an object to a datetime by parsing the previous number with less than 12 digits (

#Alternatively, the strftime() method helps to format the datetime before returning the string. In the following example, the dashes (-) between the original date times are replaced with backslashes (/), and the numeric month (02) is replaced with the abbreviated English term (Feb).

Since there are many ways to interpret dates (day, month, year) and times (hours, minutes, seconds), understand the different format codes to It's important. The table below is a cheat sheet for commonly used format codes.

2. Use time zone

A datetime object without time zone information is called "naive", an object with time zone information (usually with HH at the end :MM corresponding to GMT) is considered "aware". Probably one of the most comprehensive libraries in Python, pytz simplifies the task of time zone calculations. The following code snippet will show you how to convert between "naive" and "aware" datetime objects, and can use different time zones. The last part of the code also demonstrates how to convert the given datetime object to the local time zone. This example shows time zone codes for Japan and Germany, for other regions you can refer here.

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3. Use interval to compare two datetime(s)

Sometimes we have to compare two datetimes conditionally. Imagine that you have two dataframes, the first contains only one column of datetime and the second contains two columns representing intervals and other information in the remaining columns. Your goal is to find a matching datetime from the first dataframe if it falls within the interval of the second dataframe, and if so, copy the other columns.

One way to achieve this is to use pd.Interval to compress the interval between two datetimes and then assign them as indices of a dataframe that can later be used with Conditionally compare and map datetimes. This can be done by using a for loop to copy the columns of interest if the time condition is met.

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

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