Pandas is a Python package designed to do work with “labeled” and “relational” data simple and intuitive. Pandas is a perfect tool for data wrangling. It designed for quick and easy data manipulation, aggregation, and visualization.
There are two main data structures in the library:
“Series” – 1 dimensional
“Data Frames”, 2 dimensional
For example, when you want to receive a new Dataframe from these two types of structures. As a result, you will receive such Df by appending a single row to a DataFrame by passing a Series:
Here is just a small list of things that you can do with Pandas:
Easily delete and add columns from DataFrame
Convert data structures to DataFrame objects
Handle missing data, represents as NaNs
Powerful grouping by functionality
Another SciPy Stack core package and another Python Library that is tailored for the generation of simple and powerful visualizations with ease is Matplotlib. It is a top-notch piece of software which is making Python (with some help of NumPy, SciPy, and Pandas) a cognizant competitor to such scientific tools as MatLab or Mathematica. However, the library is pretty low-level, meaning that you will need to write more code to reach the advanced levels of visualizations and you will generally put more effort, than if using more high-level tools, but the overall effort is worth a shot.
With a bit of effort you can make just about any visualizations:
Bar charts and Histograms;
There are also facilities for creating labels, grids, legends, and many other formatting entities with Matplotlib. Basically, everything is customizable.
The library is supported by different platforms and makes use of different GUI kits for the depiction of resulting visualizations. Varying IDEs (like IPython) support functionality of Matplotlib.
There are also some additional libraries that can make visualization even easier.