Python is increasingly being used as a scientific language. Matrix and vector manipulations are extremely important for scientific computations. Both NumPy and Pandas have emerged to be essential libraries for any scientific computation in python due to their intuitive syntax and high-performance matrix computation capabilities.
Pandas has been one of the most popular and favourite data science tools used in Python programming language for data wrangling and analysis. Data is unavoidably messy in real world. And Pandas is seriously a game changer when it comes to cleaning, transforming, manipulating and analyzing data. In simple terms, Pandas helps to clean the mess. When I first started out learning Python, I was naturally introduced to NumPy (Numerical Python).
In this article, I will offer an opinionated perspective on how to best use the Pandas library for data analysis. My objective is to argue that only a small subset of the library is sufficient to complete nearly all of the data analysis tasks that one will encounter. This minimally sufficient subset of the library will benefit both beginners and professionals using Pandas. Not everyone will agree with the suggestions I lay forward, but they are how I teach and how I use the library myself. If you disagree or have any of your own suggestions, please leave them in the comments below. Pandas is the most popular Python library for doing data analysis. While it does offer quite a lot of functionality, it is also regarded as a fairly difficult library to learn well. The whole point of a data analysis library should be to provide you with the tools so that you can focus on the data analysis. While Pandas does provide you with the right tools, it doesn't do so in a way that allows you to focus on the analysis. Instead, users are forced to tread through the complex and overabundant syntax. I endorse the following as my definition for Minimally Sufficient Pandas. Pandas often gives its users multiple approaches to complete the same task.
This work is supported by Anaconda Inc. and the Data Driven Discovery Initiative from the Moore Foundation. Anaconda is interested in scaling the scientific python ecosystem. My current focus is on out-of-core, parallel, and distributed machine learning. This series of posts will introduce those concepts, explore what we have available today, and track the community's efforts to push the boundaries. I am (or was, anyway) an economist, and economists like to think in terms of constraints.