front matter
When I started to teach Python at companies around the world, I wasn’t surprised by how my students were using the language. They were typically using it the same way I was: for shell scripting in a more expressive language than Bash, writing server-side web applications, developing automated tests, and working with relational databases.
After a while, I found that students were using Python to analyze data—something I hadn’t expected. Python was powerful and easy to use, but it was also fairly inefficient. How could people use it for data analysis?
I soon learned what many others already knew: NumPy combined the ease of Python with the efficiency of C. I jumped on the NumPy bandwagon, using it for analysis and teaching courses in it. But NumPy was still a bit too low-level for my tastes.
I was thus delighted to discover pandas, which gave me the speed and efficiency of NumPy but with a rich API that made many of my daily tasks easier. I have often described pandas as being like a car’s automatic transmission, which we can contrast with the low-level manual transmission that NumPy provides. Pandas allowed me to read and write data in a variety of formats, to examine and analyze my data, to clean it, and to visualize it—in short, all the functionality I needed. I was hooked.