10 Python data visualization libraries for multiple disciplines to help you make charts

Time:2022-1-12

preface

The text and pictures of this article come from the network, only for learning and communication, and do not have any commercial purpose. If you have any questions, please contact us in time for handling.

Author: lty beautiful life

Link: https://blog.csdn.net/weixin_44208569

Today, we will introduce 10 Python data visualization libraries applicable to multiple disciplines, including famous and little-known ones.

10 Python data visualization libraries for multiple disciplines to help you make charts

 

1、matplotlib

10 Python data visualization libraries for multiple disciplines to help you make charts

 

Two histograms

Matplotlib is the leader of Python visualization library. After more than ten years, it is still the most commonly used drawing library for Python users. Its design is very close to the commercial programming language Matlab designed in the 1980s.

Since Matplotlib is the first Python visualization library, many other libraries are based on it or call it directly.

For example, pandas and Seaborn are outsourcing of Matplotlib. They allow you to call Matplotlib methods with less code.

Although the general information of data can be easily obtained with Matplotlib, it is not so easy to make charts for publication more quickly and simply.

As Chris Moffitt mentioned in “Introduction to Python visualization tools”: “it is very powerful and complex.”

Matplotlib that has a strong 90s breath of default drawing style is also make complaints about many years. The upcoming Matplotlib 2.0 claims to include many more fashionable styles.

Developer: John D. Hunter

2、Seaborn

10 Python data visualization libraries for multiple disciplines to help you make charts

 

Violinplot (Michael Waskom)

Seaborn uses Matplotlib to make good-looking charts with concise code.

The biggest difference between Seaborn and Matplotlib is that its default drawing style and color matching have modern beauty.

Since Seaborn is built on Matplotlib, you need to know about Matplotlib to adjust the default parameters of Seaborn.

Developer: Michael waskom

3、ggplot

10 Python data visualization libraries for multiple disciplines to help you make charts

 

Small multiples (ŷhat)

Ggplot is a mapping package based on R, ggplot2, and uses the concepts derived from the graph of graphics.

The difference between ggplot and Matplotlib is that it allows you to overlay different layers to complete a picture. For example, you can start from the axis, and then add points, lines, trend lines, etc.

Although image grammar has been praised for its mapping method of “approaching the thinking process”, users who are used to Matplotlib may need some time to adapt to this new way of thinking.

The author of ggplot mentioned 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 highly integrated with pandas, so when you use it, you’d better read your data as a dataframe.

Developer: ŷ hat

4、Bokeh

10 Python data visualization libraries for multiple disciplines to help you make charts

 

Interactive weather statistics for three cities (Continuum Analytics)

Like ggplot, bokeh is also based on the concept of graphic grammar.

But unlike ggplot, it is completely based on Python rather than referenced from R.

Its advantage is that it can be used to make interactive charts that can be directly used in the network. Charts can be output as JSON objects, HTML documents or interactive web applications.

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

The highest control level is used for rapid mapping, mainly for making common images, such as histogram, box chart and histogram.

The medium level of control, like Matplotlib, allows you to control the basic elements of the image (such as points in the distribution map).

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

It has no default value. You have to define each element of the chart.

Developer: continuum analytics

5、pygal

10 Python data visualization libraries for multiple disciplines to help you make charts

 

Box plot (Florian Mounier)

Like bokeh and plotly, pygal provides interactive images that can be directly embedded in a web browser.

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

If your data volume is relatively small, SVG is enough. But if you have hundreds of data points, the rendering process of SVG will become very slow.

Because all charts are encapsulated into methods, and the default style is also very beautiful, it is easy to make beautiful charts with a few lines of code.

Developer: Florian Mounier

6、Plotly

10 Python data visualization libraries for multiple disciplines to help you make charts

 

Line plot (Plotly)

You may have heard of the online mapping tool plotly, but did you know you can use it in Python?

Plotly, like bokeh, is committed to the production of interactive charts, but it provides several chart types that are difficult to find in other libraries, such as contour charts, tree charts and three-dimensional charts.

Developer: plotly

7、geoplotlib

10 Python data visualization libraries for multiple disciplines to help you make charts

 

Choropleth (Andrea Cuttone)

Geoplotlib is a toolkit for making maps and geo related data.

You can use it to make a variety of maps, such as equivalent area map, heat map and point density map.

You must install pyglet (an object-oriented programming interface) to use geoplotlib. However, because most Python visualization tools do not provide maps, it is very convenient to have a full-time map drawing tool.

Developer: Andrea cuttone

8、Gleam

10 Python data visualization libraries for multiple disciplines to help you make charts

 

Scatter plot with trend line (David Robinson)

Gleam borrowed shiny’s inspiration from 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 Python visualization library.

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

Developer: David Robinson

9、missingno

10 Python data visualization libraries for multiple disciplines to help you make charts

 

Nullity matrix (Aleksey Bilogur)

Missing data is eternal pain.

Missingno uses images to enable you to quickly assess the lack of data, rather than struggling in the data sheet.

You can sort or filter the data according to the integrity of the data, or consider revising the data according to the heat map or tree view.

Developer: Aleksey bilogur

10、Leather

10 Python data visualization libraries for multiple disciplines to help you make charts

 

10 Python data visualization libraries for multiple disciplines to help you make charts

 

Chart grid with consistent scales (Christopher Groskopf)

The best definition of leather comes from its author Christopher groskopf.

“Leather is suitable for people who need a chart now and don’t care whether the chart is perfect or not.”

It can be used for all data types and then generate SVG images so that you don’t lose image quality when you resize the image.

Developer: Christopher groskopf