Today we’ll take a look at 10 Python data visualization libraries for a variety of disciplines, some well known and some less well known.

1, matplotlib

Matplotlib is the leading Python visual library. For more than a decade it remains the most commonly used drawing library for Python users. Its design is very similar to MATLAB, a commercial programming language designed in the 1980s.

Since Matplotlib was the first Python visual library, there are many other libraries built on it or called directly from it.

For example, pandas and Seaborn are matplotlib outsourcers that allow you to call matplotlib methods in less code.

While matplotlib makes it easy to get an overview of the data, it’s not so easy to create publishably formatted charts quickly and easily. In order to help you avoid detachments and pitfalls in learning Python, you can go to the course of a Python master. He will have a free live lecture online at 8:00 every night. He will talk about Python, and his lectures are very easy to understand and funny. The greatest value of learning Python from a master is that listening to your words is better than reading ten years of books. The value of self-study lies in that it is better to learn it by yourself for more than half a year. It is not as good as others who have a master to teach you for one day, 365 days a year. To his WeiXin * (in Chinese) : in the front row is: 762, in the middle of a row is: 459, the back of a set of is: 510, the above three group of letters can be combined in accordance with the order, very simple, Newton once said, standing on the shoulders of others, you will see higher and further, all rivers run into sea, to conquer the python world of the ocean.

As Chris Moffitt noted in “Introduction to Python Visualization Tools” : “Very powerful and very complex.”

Matplotlib’s default graphics style, with its strong ’90s vibe, has been teased for years. The upcoming Matplotlib 2.0 release promises to include many more stylish styles.

Developer: John D. Hunter

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2, Seaborn

Seaborn takes advantage of Matplotlib, which uses simple code to make beautiful diagrams.

The biggest difference between Seaborn and Matplotlib is that its default drawing style and color scheme are modern.

Since Seaborn is built on matplotlib, you need to know matplotlib to adjust Seaborn’s default parameters.

Developer: Michael Waskom

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3, ggplot

Ggplot is based on ggplot2, a graphing package from R, and also utilizes concepts from The Grammar of Graphics.

Ggplot differs from Matplotlib in that it allows you to overlay different layers to complete a plot. For example, you can start with an axis, and then you can add points, you can add lines, trend lines and so on.

While Image Grammar has been praised for its “close to the thought process” approach to drawing, users used to Matplotlib may need some time to get used to this new way of thinking.

The authors of GGPlot mention that GGPlot is not suitable for making very personalized images. It sacrifices image complexity for simplicity of operation.

Ggplot is tightly integrated with pandas, so it’s best to store your data in a DataFrame when using ggplot.

Ggplot is very well integrated with pandas, so it is best to read your data as a DataFrame when using it.

Developer: ŷ Hat

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4, Bokeh

Like GGPlot, Bokeh is based on the concept of Graph Syntax.

Unlike GGPlot, however, it is based entirely on Python and not referenced from R.

Its strength is that it can be used to create interactive diagrams that can be used directly on the web. Diagrams can be output as JSON objects, HTML documents, or interactive web applications.

Boken also supports data streaming and real-time data. Bokeh offers three levels of control for different users.

The highest level of control is used for rapid mapping, mainly for making commonly used images such as bar charts, box charts, and histograms.

Medium control levels like Matplotlib allow you to control basic elements of the image (such as points in the distribution).

The lowest level of control is primarily for developers and software engineers.

There are no default values, you have to define each element of the diagram.

Developer: Continuum Analytics

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5, pygal

Like Bokeh and Plotly, Pygal offers interactive graphics that can be embedded directly into a Web browser.

The main difference from the other two is that it can output diagrams in SVG format.

If you have a relatively small amount of data, SVG will suffice. But if you have hundreds or thousands of data points, SVG rendering becomes slow.

Because all diagrams are packaged as methods, and the default style is nice, it’s easy to make beautiful diagrams with just a few lines of code.

Developer: Florian Mounier

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6, Plotly

You may have heard of the online charting tool Plotly, but did you know that you can use it through Python?

Plotly shares Bokeh’s commitment to interactive charts, but it offers several types of charts that are hard to find in other libraries, such as contour maps, trees, and 3d charts.

Developer: Plotly

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7, geoplotlib

Geoplotlib is a toolkit for creating maps and georelated data.

You can use it to make all kinds of maps, like equivalent area maps, heat maps, point density maps.

You must install Pyglet (an object-oriented programming interface) to use GeoPlotlib. But since most Python visualization tools don’t provide maps, it’s handy to have a dedicated mapping tool.

Developer: Andrea Cuttone

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8 Gleam.

Gleam borrows the inspiration for Shiny in R. It allows you to turn your analysis into an interactive web application using only Python programs. You don’t need to be able to use HTML CSS or JaveScript.

Gleam can use any of Python’s visual libraries.

When you create a chart, you can add a field to it so that users can sort and filter data.

Developer: David Robinson

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9 missingno.

Missing data is a permanent pain.

Missingno uses graphics to allow you to quickly assess missing data, rather than trudging through tables.

You can sort or filter data based on its completeness, or consider revising data based on heat maps or trees. In order to help you avoid detachments and pitfalls in learning Python, you can go to the course of a Python master. He will have a free live lecture online at 8:00 every night. He will talk about Python, and his lectures are very easy to understand and funny. The greatest value of learning Python from a master is that listening to your words is better than reading ten years of books. The value of self-study lies in that it is better to learn it by yourself for more than half a year. It is not as good as others who have a master to teach you for one day, 365 days a year. To his WeiXin * (in Chinese) : in the front row is: 762, in the middle of a row is: 459, the back of a set of is: 510, the above three group of letters can be combined in accordance with the order, very simple, Newton once said, standing on the shoulders of others, you will see higher and further, all rivers run into sea, to conquer the python world of the ocean.

Developer: Aleksey Bilogur

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10, Leather

The best definition of Leather comes from its author Christopher Groskopf.

“Leather is for people who need a chart right now and don’t care if it’s perfect.”

It can be used for all data types and then generate SVG images so that you can resize the image without losing quality.

Well, everyone, that’s all for this article. If you want to know more about programming, please go to the official website of Six Star Education!