In the process of data analysis and processing, data visualization is a very important part. As an excellent computer language, Python naturally has a wealth of data visualization tools. This article will introduce some commonly used Python data visualization tools, hoping to provide some help for you.
A large collection of Python data visualization tools
Matplotlib：
Matplotlib is a Python 2D plotting library and one of the most commonly used data visualization tools in Python . It provides very rich drawing options, and can draw various types of charts such as line graphs, scatter plots, histograms, and pie charts. At the same time, Matplotlib also supports custom colors, styles, labels and other drawing properties, allowing users to easily create beautiful and high-quality charts.
It produces publication-quality graphics in a variety of hardcopy formats and in a cross-platform interactive environment. Matplotlib is available for Python scripts, Python and IPython shells, Jupyter notebooks, web application servers, and four GUI toolkits.
Matplotlib tries to make easy things easier and hard things possible, generating graphs, histograms, power spectra, bar plots, error plots, scatterplots, and more with just a few lines of code.
For simple plotting, the pyplot module provides a MATLAB-like interface, especially when combined with IPython. For advanced users, you have full control over line styles, font attributes, through an object-oriented interface or a set of features familiar to MATLAB users , axis properties, etc.
Official website: matplotlib.org.cn/
Sea born:
Seaborn is an advanced data visualization tool based on Matplotlib , specially designed for drawing statistical charts and infographics. It provides a more beautiful, easy-to-read diagram style, suitable for direct use in presentations and presentations. Seaborn also supports custom colors, styles and other attributes, which can help users better present data.
Seaborn can be used to explore data. Its plotting functions operate on data frames and arrays containing entire datasets, and internally perform the necessary semantic mapping and statistical aggregation to generate infographics. Its dataset-oriented declarative API can focus on The meaning of the different elements of the drawing, not the details of how to draw them.
Matplotlib has a comprehensive and powerful API , and you can change almost any attribute of the graph according to your preferences. The combination of seaborn's advanced interface and matplotlib's deep customizability makes Seaborn not only quickly explore data, but also create custom-made publication-quality graphs. Graphics of the final product.
Official website: seaborn.pydata.org/
Plotly：
Plotly is an interactive data visualization tool that generates dynamic, interactive charts. It supports various types of graphs, including scatter plots, line graphs, heat maps, maps, etc., and also supports custom colors, styles, and other attributes. Plotly also provides an online visualization platform where users can share and display their own charts.
Plotly supports the creation of static graphs and interactive graphs. Static graphs are suitable for data analysis reports, while interactive graphs are more suitable for use in web applications and data dashboards, and also support use in jupyter notebooks for data analysis and interactive data visualization.
Plotly is relatively easy to get started, and there are many chart cases on its official website that can be used directly, which greatly facilitates readers to do data analysis and is suitable for data analysts.
Official website: plotly.com/python/stati...
Bokeh：
Bokeh is another interactive data visualization tool that can also generate dynamic, interactive charts. It supports many types of charts, including scatter plots, line charts, histograms, contour plots, and more. The advantage of Bokeh is that it can display charts directly in a web browser. Users can interactively explore data through scrolling, dragging, etc. data sources, etc. This interactive visualization allows users to gain a deeper understanding of the data and discover patterns and trends in the data.
Bokeh supports a variety of output methods, and can save charts as HTML files, PNG images, SVG vector graphics, etc.
At the same time, Bokeh also supports directly displaying charts in Jupyter Notebook, which is convenient for users to analyze and display data.
Bokeh is easy to use and requires only a few lines of Python code to create an interactive chart. Moreover, Bokeh's official documentation is detailed and easy to understand, and there are rich sample codes for reference. In addition, Bokeh also provides some visualization tools, such as Bokeh Server, which allows users to interact with data through Web pages.
Official website: bokeh.org/
Pygal：
Pygal is an easy-to-use SVG chart generation tool that supports many types of charts, including line charts, bar charts, pie charts, maps, etc. The chart generated by Pygal has a good responsive design, which can adapt to different sizes of screens and devices, and also supports custom colors, styles and other attributes.
1. Line chart
Line charts are one of the most commonly used chart types in Pygal. The change trend of the data can be clearly displayed through the line chart. Pygal can automatically generate a line chart based on the data, and can customize the chart according to user needs.
2. Histogram
A histogram is a type of chart used to compare differences between different data. Pygal can generate various types of histograms for users, and can adjust the color, size and other parameters of the histogram to make it more in line with user needs.
3. Pie chart
A pie chart is a type of chart used to display proportions of data. Pygal can automatically generate pie charts, and provides a wealth of style and configuration options, users can easily customize pie charts to their desired style.
In short, Pygal is a powerful, easy-to-use data visualization library. It supports a variety of chart types and provides a wealth of configuration options, allowing users to easily create the chart effects they want. For users who need data visualization, Pygal is a rare tool.
Official website: pygal.org/
Summarize
Python data visualization tools are rich and diverse, and the above editors only introduce some commonly used tools. Choosing the tools that suit us can help us better understand and display data, so as to conduct better data analysis and research.