Introduction Python Pandas

Pandas is an open-source Python Library providing high-performance data manipulation and analysis tool using its powerful data structures. The name Pandas is derived from the word Panel Data – an Econometrics from Multidimensional data.

In 2008, developer Wes McKinney started developing pandas when in need of high performance, flexible tool for analysis of data.

Prior to Pandas, Python was majorly used for data munging and preparation. It had very little contribution towards data analysis. Pandas solved this problem. Using Pandas, we can accomplish five typical steps in the processing and analysis of data, regardless of the origin of data — load, prepare, manipulate, model, and analyze.

Python with Pandas is used in a wide range of fields including academic and commercial domains including finance, economics, Statistics, analytics, etc.

Key Features of Pandas
Fast and efficient DataFrame object with default and customized indexing.
Tools for loading data into in-memory data objects from different file formats.
Data alignment and integrated handling of missing data.
Reshaping and pivoting of date sets.
Label-based slicing, indexing and subsetting of large data sets.
Columns from a data structure can be deleted or inserted.
Group by data for aggregation and transformations.
High performance merging and joining of data.
Time Series functionality.
Python Pandas - Environment Setup
Standard Python distribution doesn't come bundled with Pandas module. A lightweight alternative is to install NumPy using popular Python package installer, pip.

pip install pandas
If you install Anaconda Python package, Pandas will be installed by default with the following −

Windows
Anaconda (from https://www.continuum.io) is a free Python distribution for SciPy stack. It is also available for Linux and Mac.

Canopy (https://www.enthought.com/products/canopy/) is available as free as well as commercial distribution with full SciPy stack for Windows, Linux and Mac.

Python (x,y) is a free Python distribution with SciPy stack and Spyder IDE for Windows OS. (Downloadable from http://python-xy.github.io/)

Linux
Package managers of respective Linux distributions are used to install one or more packages in SciPy stack.

For Ubuntu Users

sudo apt-get install python-numpy python-scipy python-matplotlibipythonipythonnotebook
python-pandas python-sympy python-nose
For Fedora Users

sudo yum install numpyscipy python-matplotlibipython python-pandas sympy
python-nose atlas-devel