What is pandas? =============== .. figure:: img/pandas_logo.png :class: dark-light :width: 300px Source: `Medium.com `__ `pandas `__ is a modern, powerful and feature-rich library that is designed for doing data analysis in Python. It is a mature data analytics framework (originally written by Wes McKinney) that is widely used among different fields of science. Thus, documentation and many good examples exist that can help you get rolling with your data analysis tasks. Easy-to-use data structures --------------------------- In ``pandas``, the data is typically stored in a data structure called a ``DataFrame`` that looks like a typical table with rows and columns (+ indices and column names), where columns can contain data of different data types. Thus, it is similar in some sense to how data is stored in Excel or in R, which also uses a concept of a data frame. In fact, Wes McKinney first developed ``pandas`` as an alternative for R to deal with different complex data structures. Combines functionalities from many Python modules ------------------------------------------------- ``pandas`` takes advantage of the `NumPy `__ module under the hood, which is mostly written in C. This makes it a fast and powerful library that can efficiently handle even very large datasets. ``pandas`` offers an easier and more intuitive syntax to do data analysis and manipulation using either ``numpy`` functionalities in the background or dedicated functionalities written explicitly for ``pandas``. However, ``pandas`` is much more than an easier-to-use ``numpy`` as it also combines many functionalities from other Python libraries such as `Matplotlib (plotting) `__ and `SciPy (mathematics, science, engineering) `__. Thus, you can use many of the features included in those packages without importing them at all. Supports data read/write from multiple formats ---------------------------------------------- One of the most useful features of ``pandas`` is its ability to read data from numerous different data formats directly. For example, ``pandas`` supports reading and writing data from/to: - CSV - JSON - HTML - MS Excel - HDF5 - Stata - SAS - Python Pickle format - SQL (Postgresql, MySQL, Oracle, MariaDB, etc.) You can view the full list of supported data formats from the `pandas documentation `__.