Python is the most trending language now a day and many task realted to analytics, machine learning are performed in python only. So today will give you an Introduction to Data Visualization in Python.
Data visualization is the discipline of trying to understand data by placing it in a visual context so that patterns, trends and correlations that might not otherwise be detected can be exposed.
Python offers multiple great graphing libraries that come packed with lots of different features. No matter if you want to create interactive, live or highly customized plots python has an excellent library for you. Introduction to Data Visualization in Python. To learn more visit HawksCode and Easyshiksha.
Firstly you will learn the importance of Data Virtulization in real world. Let's look at an example below.
We are a sales manager in a leading global organization. The organization plans to study the sales details of each product across all regions and countries. This is to identify the product which has the highest sales in a particular region end up the production.This research will enable the organization to increase the manufacturing of that product in the particular region. The data involved in this research might be huge and complex. The research on this large numeric data is difficult and time-consuming when it is performed manually.
When this numerical data is plotted on a graph for converted to charts it's easy to identify the patterns and predicted the result accurately.
The main benefits of data visualization are as follows:
Three major considerations for Data Visualization:
Clarity ensures that the data set is complete and relevant. This enables the data scientist to use the new patterns yield from the data in the relevant places.
Accuracy ensures using appropriate graphical representation to convey the right message.
Efficiency uses efficient visualization technique which highlights all the data points.
There are some basic factors that one would need to be aware of before visualizing the data.
Visual Effect includes the usage of appropriate shapes, colors, and size to represent the analyzed data.
The Coordinate System helps to organize the data points within the provided coordinates.
The Data Types and Scale choose the type of data such as numeric or categorical.
The Informative Interpretation helps create visuals in an effective and easily interpreted ill manner using labels, title legends, and pointers.
So far we have covered what data visualization is and how it helps interpret results with large and complex data. With the help of the Python programming language, we can perform this data visualization.