Data Science with Python
Date-time data is one of the most common and time-consuming data types you’ll work with.
Whether doing a time-series analysis, preparing the data for a machine learning model, or simply doing an exploratory data analysis, you need to work with such date-time values present in the data.
Moreover, when you combine the data from multiple sources, the date-time data is present in different formats. And to maintain data consistency, you need to transform all the date-time values to a single/standard format and therefore you must know these techniques to work with them.
You must have spent at least five minutes searching for how to manipulate such date-time data. And that too every time you work with such date-time data. I ALWAYS SPEND TIME ON THIS!!
Therefore, in this article, I’m sharing 3 quick tricks to work with date-time data in Python, using which you can fulfill most of your date-time data processing. I recommend saving this story for your future quick reference.
In Python, you can use a variety of modules such as calendar,
datetime, and time. Although each module is useful in distinct scenarios, I found the
datetime module versatile as it offers methods to work with both — dates and time.
So, here you’ll learn more about this
datetime module and how to efficiently use its methods to transform date-time data.
You can download a complete notebook with all these examples and other resources at the end of this read!
Before starting with the tips and tricks, let’s quickly review what the date-time values obtained from a
datetime module look like.
You always get the current date and time using the
datetime class within
datetime module as shown below.
from datetime import datetime
current_time = datetime.now()
datetime.datetime(2023, 7, 8, 19, 40, 6, 832421)
Well, you can see the output doesn’t look like a date but simply looks like a set of…