The Pandas library provides an array of functions for managing tabular data. It works alongside NumPy, which supports efficient numerical operations on large arrays. Furthermore, its functionality is highly extendible; it can read/write from CSV files, Excel spreadsheets, SQL databases and even Python dictionaries and lists.
Pandas has one of the most useful objects available - Series. A Series allows users to easily create two-dimensional DataFrames that store rows and columns of data. Akin to NumPy arrays, Series offers additional flexibility by labeling values that may contain row or column indexes or datatypes - an invaluable feature when dealing with large amounts of information that needs storing at once.
Prior to using Pandas, Python must first be installed. You can find and download the latest version from their official website or use a package manager tool such as pip to do this. In either case, make sure that the correct installation exists for your OS (to determine this simply type "which python in terminal on Linux or Mac OS).
Once your Python environment is running smoothly, Pandas installation can be accomplished with one command in your terminal: this will download Anaconda3, a distribution of Python that includes Pandas library. After it has finished installing, simply open either your Python shell or Jupyter Notebook and type import Pandas for importation into it.