Building Analytics Workf lows Using Python and Pandas - Data Just Right: Introduction to Large-Scale Data and Analytics - page 175

programming language provides useful benefits, such as access to a suite of well-
supported networking libraries, Web application stacks, testing and development tools,
and, most importantly, a large pool of knowledgeable developers.
NumPy provides Python with a fast multidimensional array data type along with
broadcasting functions. NumPy also provides a strong foundation for other data-
analytics modules such as SciPy. The Pandas library adds an additional layer of tooling
for common data-analysis tasks, working with time series data, dealing with missing
data, and much more. Pandas's DataFrame, inspired by a similar data type available in
R, is very flexible and provides a large number of useful data methods.
Finally, iPython provides a useful interactive and collaborative environment for sci-
entific computing. Taken as a whole, the tools provided by modules such as NumPy,
SciPy, Pandas, iPython, and a number of other data libraries bridge the gap between
interactive data-analysis workf lows and more robust programming models. It's possible
to interactively analyze data from the command line and then share and develop this
work collaboratively. Although the tools available to the Python community lack some
of the features and scientific models available for R or MATLAB, Python's advantages,
along with the very active developer community, make it an excellent choice for many
data-analytics challenges.