3 Easy to Use Python Data Visualization Libraries for Beginners

Data by itself is not biased but interpretations can be. But in this information age, we need interpretations to run our daily life. And we hence we need advanced visualizations that aid impartiality more than ever before.

As Python becomes one of the most widely used language for data analysis and visualizations, lets look at the cream of all the visualization libraries available in Python.

  1. Plotly

Are you looking for something simple and interactive that is compatible with different file formats? Then Plotly is for you. Plotly offers a variety of chart styles like bar charts, histograms, scatter plots etc. with easy syntaxes for your basic needs. It has a web-based interface and can be accesses through your Python notebook, which makes it easily shareable as well.

However, with simplicity comes limitations too. Most visualizations can be viewed publicly and the color palette is limited. However, if you are a beginner in Python data visualizations or if data privacy is not your greatest concern, get in there and play with it.

2. Pygal

If your data-set is on the smaller side in terms of size (not hundreds of thousands data points) and you want visually pleasing visualization then Pygal is for you. It produces several styles of charts like histograms, line, box, treemap, pyramidi etc without writing a lot of code.

The Pygal library renders charts of Scalable Vector Graphics file. SVG files are XML based and your standard browser and text editor can open them. This also allows them to be interactive,  indexed, scripted, compressed and searched easily.

3. Seaborn

Matplotlib is the oldest two-dimensional Python data visualization library. It is highly efficient and useful but also confusing when we try to implement complex visualizations, it can be a little confusing with its MATLAB like interface.

Seaborn is based on Matplotlib but reduces its complexity a great deal and offers a variety of popular chart styles like heat maps, time-series charts, density estimates etc. The availability of popular chart styles that are aesthetically pleasing without writing complex codes has made Seaborn quite popular.

Check out ‘How Picasso Helped Me Scale Up My Data Visualizations‘ to read one analyst’s journey in data visualization.

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