Hey guys! In this blog post, we will take a step-by-step tour to learn how we can manipulate the timestamps using the Python data analysis library - Pandas. Pandas is a fast, flexible, and easy-to-use open-source data analysis and manipulation tool built on top of the Python programming language.
At Outline India (hereafter, OI), we extensively use the SurveyCTO data collection platform to code our paper-based tools in digital format to conduct various field surveys. After each completed survey, OI researchers use the raw dataset file (in .csv document format) with multiple variables to perform the analysis using Pandas. Every raw dataset generated using SurveyCTO contains 3 mandatory timestamp variables – Submission Date, start time, and end time.
In this blog post, we will enquire about the various steps involved, from installing the pandas to carrying out the need-based data-time manipulation using in-built timestamp functions in the Pandas library.
So let's get started.
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