Accounting for the style and color, when using in the Seaborn library

Below you can view the code used to create advanced grid visualizations in Python, by adjusting the style and color as well, using Seaborn, in the Jupyter environment.

Grids, Style and Colour using Seaborn

A screenshot of the environment is depicted below:

2017-10-24 12_34_12-Grids, Style and Colour using Seaborn
Inside the Jupyter Notebook using advanced visualization techniques

Color palette for Matplotlib

Visit the link below, to get access to the full palette of Matplotlib. Once you set the plot that you want to visualize, you can add as an additional argument to the function, one of the strings described in this site. There is also sample code that shows exactly the structure of the function.

2017-10-24 12_25_29-color example code_ — Matplotlib 2.0.2 documentation.png
Sample palette

via color example code: — Matplotlib 2.0.2 documentation

Seaborn library is another library to visualize data in Python

Seaborn is suited for statistical analysis (statistical plotting library) and is based on matplotlib library. It offers a wide variety of beautiful default styles, like shown below:

2017-10-23 19_22_59-Example gallery — seaborn 0.8.1 documentation
Seaborn Gallery

It can be integrated and used in conjunction with the Pandas library and in the link below, you can access the documentation site of the library and the most important tab of it – the gallery, from which you can grab the code for some beautiful and  stylish visualizations from each plot and figure.

via Example gallery — seaborn 0.8.1 documentation

Matplotlib is the most popular plotting library for Python

You can achieve great visualizations in Python, by importing the matplotlib library into your environment. This library is designed to feel like the Matlab environment, thus, the similar naming convention. The gallery tab demonstrates most of the capabilities of the library, along with the documentation of the respective source code.

Visit the link below for diving deeper:

via Gallery — Matplotlib 2.1.0 documentation

Visualizations in a Video Games data set

An exploratory analysis in Tableau using data for video games, spanning from 1984 to 2017, retrieved from Kaggle. For those interested in the visualization, it can be found here:

Video Games Analysis

The raw data has been retrieved from this link, but the necessary pre-processing steps have been performed using R:

Video Games Raw data set


Interacting with Level of Detail Calculations

Level of Detail calculations are really important in Tableau and can provide a suitable alternative for handling the granularity/aggregation trade-off, when visualizing data in different layers. Below, you can follow the link, to such an endeavor:

Interacting with Level of Detail Calculations | Tableau Public