Pandas is a Python package that provides fast and flexible data structures designed to make working with "relational" or "labeled" data easy and intuitive. Its goal is to be the fundamental, high-level building block for doing practical analysis of real-world data in Python. Furthermore, it has the larger goal of becoming the most powerful, flexible, and available in any language open source data manipulation/analysis tool. The two main data structures in Pandas are: Series for one-dimensional data and DataFrames for two-dimensional data. Both frameworks handle the vast majority of typical use cases in finance, statistics, social sciences, and many areas of engineering. For R users, the DataFrame provides everything that R data.frame offers, and much more. pandas is based on NumPy and is designed to integrate well into a scientific computing environment with many other third-party libraries. Pandas facilitates the work in Data Science. For data scientists, working with data is typically divided into several stages: collecting and cleaning data, analyzing/modeling it, and then organizing the analysis results in a form suitable for graphing or displaying in tabular form. pandas is a help tool for all these tasks. Also Pandas has been widely used in the production of financial applications. Also pandas works with big data
