In this article, we will cover pandas DateTime features and delve into a pandas to_datetime example, and start working with time series datasets.
(https://www.statista.com/statistics/793628/worldwide-developer-survey-most-used-languages/) use it regularly for data science-related tasks. And it is the vast variety of python libraries that need to be credited for this. Pandas is one such data analysis library.
It is purely written in C or python, making way for highly optimized back-end source code. Moreover, python has clear syntax and a low learning curve, ideal for beginners. One can implement anything from micro-projects to macro enterprises with the knowledge of this language.
Python developers who have an adequate level of knowledge and experience with the Pandas library are also in demand for several data science jobs, including data analyst, business analyst, and machine learning engineer. These career trajectories require candidates to be familiar with statistics, big data analytics, predictive analytics (using python), visualization, etc.
Engineering students interested in analytics can, therefore, go for specialized degrees that emphasize these skills. You can either pursue short-term certifications like the PG Diploma in Data Science from IIIT-Bangalore or consider joining globally accredited programs, such as M.Sc. Data Science from Liverpool John Moores University (LJMU), England.
Now that we’ve given you a brief background of the relevance of python and pandas in the modern-day tech field, let’s begin our step-by-step tutorial on pandas DateTime.
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