The basic version of pandas was built in 2008 by Wes McKinney, an MIT grad with heavy quantitative finance experience. Now a celebrity in his own right, thanks to his open source contributions and the wildly popular book called Data Analysis with Python, he was reportedly frustrated with the time he had to waste doing simple data manipulation tasks at his job, such as reading a CSV file, with the popular tools at that time. He said he quickly fell in love with Python for its intuitive and accessible nature after not finding Excel and R suitable for his needs. But he found that it was missing key features that would make it the go-to tool for data analysis—for example, an intuitive format to deal with spreadsheet data or to create new calculated columns from existing columns.

According to an interview he gave to Quartz, the design considerations and vision that he had in mind while creating the tool were the following:

• Quality of data is far more important than any fancy analysis
• Treating in-memory data like a SQL table or an Excel spreadsheet
• Intuitive analysis and exploration with minimal and elegant code
• Easier compatibility with other libraries used for the same or different steps in the data pipeline

After building the basic version, he went on to pursue a PhD at Duke University but dropped out in a quest to make the tool he had created a cornerstone for data science and Python. With his dedicated contribution, together with the release of popular Python visualization libraries such as Matplotlib, followed by machine learning libraries such as Scikit-Learn and interactive user interfaces such as Jupyter and Spyder, pandas and eventually Python became the hottest tool in the armory of any data scientist.

Wes is heavily invested in the constant improvement of the tool he created from scratch. He coordinates the development of new features and the improvement of existing ones. The data science community owes him big time.