Exploring the history and evolution of pandas
pandas, in its basic version, was open sourced in 2009 by Wes McKinney, an MIT graduate with experience in quantitative finance. He was unhappy with the tools available at the time, so he started building a tool that was intuitive and elegant and required minimal code. pandas went on to become one of the most popular tools in the data science community, so much so that it even helped increase Python's popularity to a great extent.
One of the primary reasons for the popularity of pandas is its ability to handle different types of data. pandas is well suited for handling the following:
- Tabular data with columns that are capable of storing different types of data (such as numerical data and text data)
- Ordered and unordered series data (an arbitrary sequence of numbers in a list, such as [2,4,8,9,10])
- Multi-dimensional matrix data (three-dimensional, four-dimensional, and so on)
- Any other form of observational/statistical data (such as SQL data and R data)
Besides this, a large repertoire of intuitive and easy-to-use functions/methods makes pandas the go-to tool for data analytics. In the next section, we'll cover the components of pandas and their main applications.
