The pandas is a high-performance open source library for data analysis in Python developed by Wes McKinney in 2008. Over the years, it has become the de-facto standard library for data analysis using Python. There's been great adoption of the tool, a large community behind it, (220+ contributors and 9000+ commits by 03/2014), rapid iteration, features, and enhancements continuously made.
Some key features of pandas include the following:
It can process a variety of data sets in different formats: time series, tabular heterogeneous, and matrix data.
It facilitates loading/importing data from varied sources such as CSV and DB/SQL.
It can handle a myriad of operations on data sets: subsetting, slicing, filtering, merging, groupBy, re-ordering, and re-shaping.
It can deal with missing data according to rules defined by the user/developer: ignore, convert to 0, and so on.
It can be used for parsing and munging (conversion) of data as well as modeling and statistical analysis.
It integrates well with other Python libraries such as statsmodels, SciPy, and scikit-learn.
It delivers fast performance and can be speeded up even more by making use of Cython (C extensions to Python).
For more information go through the official pandas documentation available at http://pandas.pydata.org/pandas-docs/stable/.