Working with Dates
Explore techniques to preprocess and standardize date and time data within text datasets. Learn how to extract components like day, month, year, and time of day to create useful features, enhancing your ability to incorporate temporal information in natural language processing and analysis.
We'll cover the following...
Introduction
When working with text data, it’s common to come across dates and times, which can provide valuable information for machine-learning models. However, working with that data can be challenging because it comes in various formats that might not be suitable for further analysis. For example, in a dataset that contains reviews, a timestamp column might contain dates and times indicating when the reviews were submitted. These timestamps could be in formats like YYYY-MM-DD HH:MM:SS or MM/DD/YYYY HH:MM AM/PM, requiring preprocessing and standardizing. Working with such data might involve extracting relevant components from dates, such as day of the week, month, year, quarter, season, etc., which we can use as features in analysis. It might also involve parsing date strings from different formats (e.g., YYYY-MM-DD, MM/DD/YYYY) into a standardized format to ensure consistency and compatibility for analysis.
Date data origins
Date and time data can originate from diverse sources and methods, and understanding how to manage this data is crucial, as ...