You’re a data visualization professional. What are the most important libraries you need to know?

Matplotlib: uma biblioteca amplamente utilizada para criar gráficos estáticos, como gráficos de linhas, barras e dispers?o. Seaborn: uma biblioteca que aprimora a aparência dos gráficos do Matplotlib e oferece recursos adicionais, como gráficos de densidade e gráficos de caixa. Plotly: uma biblioteca interativa que permite criar gráficos interativos, mapas e visualiza??es de dados dinamicos, adequados para dashboards e aplica??es web.

Some ways to get started with Python libraries for data visualization: Learn the basics of "Matplotlib" and "Seaborn" in the initial stages. Try to explore different ways of representation of single visualization, this would help in getting to know the libraries more. For interactive plots, use "Plotly". This library provides options of zoom-in, zoom-out, which can be useful if the plot is complex and contains a lot of data.

Matplotlib and Seaborn are the main libraries used for data viz. I will recommend for newbies to start with Matplotlib with the command: import matplotlib.pyplot as plt. That will give the first idea in code terms to know how to visualize data (combined with pandas is a powerful tool) As a second step I recommend Seaborn. Is a library based on matplotlib and is intended to have more sofisticated visuals and give more tools to be a more specialized data viz professional.

As a data visualization professional, it's essential to be familiar with several important libraries to work efficiently. Here are some of the key ones: ??Matplotlib. ??Seaborn. ??Plotly. ??Bokeh. ??Pandas Plotting. ??ggplot (ggpy. ??Altair. ??D3.js. These are just some of the most important libraries you might want to know as a data visualization professional. The choice of library depends on your specific needs and personal preferences in terms of visualization style and functionality.

Matplotlib of course! But Pandas, numpy, scipy are all pretty important to manipulate data before visualization. Also Jupyter notebook has a couple awesome extensions like ipyvolume and ipyleaflet.

ggplot2 is a popular data visualization package for the R programming language, widely used for its ability to create complex and visually appealing graphics. Designed based on the principles of "The Grammar of Graphics," ggplot2 allows you to create charts gradually by adding layers of visual information. This approach makes it very flexible and powerful.

D3.js frees you from the "wall of abstraction", which is that wall you hit when you try to do something in a higher level tool that the tool authors didn't foresee. In this sense, building with D3 gives you unlimited potential to customize your visualizations and add arbitrarily complex interactivity. It also interoperates well with popular frameworks such as React, Vue, and Svelte. Most of my client projects are React apps that use D3 for the visualizations. Would highly recommend this stack!

Effective data visualization requires not just knowledge with a wide range of libraries, but also an understanding of visual communication concepts and their application to the creation of precise, accurate, and insightful representations. It will be helpful to try out various libraries and projects and stay current with industry developments and new technologies.

List of tools and libraries are growing, important aspect is continuous learning. Also pick the platform or tool best for the purpose.

To have good visualizations it is important: - To be sure what to measure and how. - Use the correct visualization according to the data. - Hands on code!