Pandas is a Python package providing fast, flexible, and expressive data structures designed to make working with “relational” or “labeled” data.
Dataset, it aims to be the fundamental high-level building block for doing practical, real world data analysis and has the broader goal of becoming the most powerful and flexible open source data analysis / manipulation tool available in any language. (For R users, DataFrame provides everything that R’s data.frame provides and much more.) Also, pandas is built on top of NumPy and is intended to integrate well within a scientific computing environment with many other 3rd party libraries.
Here are just a few of the things that both Pandas and
Dataset do well:
- Easy handling of missing data (represented as NaN) in floating point as well as non-floating point data
- Size mutability: columns can be inserted and deleted from DataFrame and higher dimensional objects
- Label-based slicing, fancy indexing, and subsetting of large data sets
- Intuitive merging and joining data sets
Has anyone seen a more complete comparison of the pros and cons of each?