Last updated on October 10th, 2023 at 11:21 am
Data visualization has become an increasingly important part of the data analysis process in recent years. Many analysts have found that a picture is worth a thousand words, and in this case, it just might be true. You could say that good data visualization can save even more than 1,000 words--it can save lives! Let's explore some basics of making compelling visualizations with Python.
What is Data Visualization?
Data visualization represents data in a visual form. You can use visualizations to help people understand data more efficiently, ranging from simple graphs to complex infographics. Data visualization is an increasingly popular field with many practical applications. For example, you can use it for business intelligence gathering and analysis or education purposes. Some experts consider data visualization to be a vital part of the expanding field of big data.
Data types and how they get visualized?
There are many types of data, including categorical, univariate, multivariate normal, and so on. Data visualization methods vary depending on the type of data represented. For example, there are several other ways to express categorical data than with graphs.
Univariate data is usually best displayed in a simple bar graph or line graph. Categorical information is often best represented by a pie chart. Multivariate data can be shown in a radar graph or spider chart, while multivariate average data get visualized with a scatter plot.
How to use Python for data visualization?
Python is an easy-to-use programming language that You can use for data visualization. Many libraries, including matplotlib, make it possible to create visualizations without much technical knowledge.
You can even create interactive online visualizations using Python. For example, you can use Python to create visualizations for the Vega-Lite specification, which allows you to create interactive online data visualization. Due to its flexibility and ease of use, it has become one of the most popular languages for data science. It is perfect for working with large amounts of data because it can easily handle large lists or arrays.
Python-based data visualization libraries are beneficial because they typically allow for rapid prototyping of visualizations. It makes them an excellent choice for exploratory data analysis because you can quickly try out different algorithms and processes. The downside is that they can sometimes be challenging to use for more complex projects.
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