pandas is a Python package that furnishes an enhanced library for handling data structures designed to be expressive. pandas assists in carrying out practical, real world data analysis in Python with powerful and flexible open source analytics / manipulation tools. pandas is a relatively new Python library developed for data manipulation. Pandas takes data and creates a Python object with rows and columns referred to as being a data frame that looks very similar to Excel style tables. These data structures permit operations necessary for numerical manipulation of tables and time series. pandas is short for "panel data", an econometrics term commonly applied to data sets spanning multiple time periods for the same attribute. ongitudinal data, Some times referred to as longitudinal data that contains observations relating to different cross sections across time. According to Wikipedia, Wes McKinney started building what would become pandas at AQR Capital while he was a researcher there from 2007 to 2010. pandas share a number of similarities with R. The R programming language provides the data.frame data structure and multiple packages, such as tidyverse use and extend data.frame for convenient data handling functionalities similar to pandas. Likewise, the column and row organisation characteristic of Excel transfers readily to pandas. Typical applications would include opening: a local file e.g. delimited text file, Excel file or CSV file / accessing a database or website through a URL / accessing a SQL table / database / converting a Numpy array to a Pandas data frame