A Few Tips on How to Make Your Visualizations Look More Professional and Aesthetically Pleasing using Plotly

From different customizations to building interactive visualizations, below I highlight a few examples of how to make your visualizations look more professional and aesthetically pleasing using Plotly’s graphing library . Before getting started, below is a brief overview of Plotly.

What is Plotly?

Plotly developes online data analysis and interactive visualization tools. It provides graphing libraries for several programming languages. Plotly Express is a Python visualization library, which, with its terse, consistent, and high-level Application Programming Interface(API), allows for the creation of interactive and aesthetically pleasing visualizations with a few lines of code.

To get started, make sure to import Ploty’s libraries. For…


With the advent of higher computing power, machines are now able to perform tasks that were believed to be possible only by humans. Machines are now capable of beating the best humans in Chess and GO, games that were once thought impossible for computers to master. Below I highlight a few watershed moments in Machine Learning History.

Deep Blue

World chess champion Garry Kasparov playing IBM’s Deep Blue computer in 1997 © AFP

The Deep Blue was a chess playing computer developed by IBM. Development began in 1985 under the name Deep Thought and in 1989 it was renamed Deep Blue. …


As a beginner programmer with an Excel background, I often found it hard to execute in Python common tasks that I was familiar executing in Excel. When trying to do common tasks such as changing a columns’ format, filtering a column to a subset of rows, creating new columns based on information from other columns, etc., I often ran into errors and became frustrated in the process.

Python is very powerful and has many advantages over Excel including dealing with large datasets and being capable of automating workflow, just to name a few. …


“A picture is worth a thousand words” — Unknown

Innate in humans is a desire to understand the world around them. Visualizations, which involves producing images that communicate relationships among the represented data, provide a better way of understanding complexities in our surroundings. From maps of earth’s surfaces to celestial bodies visible on the sky’s above, visualizations aided human’s desire to explore and understand their environment. As societies evolved and new ways of recording information emerged, visualizations aided in making better sense of this new information. Below I highlight some important milestones in the evolution of data visualization.

Prehistoric Visualizations

15,000–13,000 B.C.E

Edward De Jesus

Data Science Student

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