The term Data Science has been coined in 2001, and this relatively new field’s popularity and acceptability have grown over time as a result of its ability to assist organizations of all sizes to uncover patterns in data, allowing them to explore new markets, control expenses, improve operational efficiency, and gain a competitive edge. As a matter of fact, a recent study shows that 47 percent of the surveyed companies have noticed a significant change in the way they compete on the market as a result of data analytics utilization, while around 62 percent of retail companies say it has provided a competitive advantage.

Around 75% of the data scientists say they frequently use python – an open-source, object-oriented, interactive, and portable programming language that is easy-to-use, mostly due to its simple syntax. There exist various libraries within the Python framework, one of which is pandas – a fast, efficient, and flexible data manipulation and analysis tool. Pandas is a must-know library for anyone who is in the data science field, from beginners to professionals.

Let’s take a look at some of its most commonly used functions.