Commonly used Python data visualization gadgets, the following are recommended. After you become proficient in using them, data visualization is no longer a problem. Moreover, these data visualization libraries can learn from each other when using them, and maximize the expression of data information. Let’s learn about it together. , what data visualization libraries are there? Can help us present data better.
1. Matplotlib: basic drawing library
Official website: https://www.matplotlib.org.cn/
Matplotlib is a Python 2D plotting library that 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 graphical user interface toolkits.
Matplotlib tries to make easy things easier and hard things possible, allowing you to generate charts, histograms, power spectra, bar graphs, error plots, scatter plots, 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 complete control over line style, font attributes, through an object-oriented interface or a set of features familiar to MATLAB users. , axis properties, etc.
2. Seaborn: Advanced matplotlib
Official website: http://seaborn.pydata.org/
Seaborn is a Python data visualization library built on top of matplotlib and tightly integrated with Pandas data structures to provide a high-level interface for drawing attractive and informative statistical graphics.
Seaborn can be used to explore data. Its plotting functions operate on data frames and arrays containing the entire dataset and internally perform the necessary semantic mapping and statistical aggregation to generate information graphs. Its declarative API for datasets can focus on The meaning of the different elements of drawing, rather than the details of how to draw them.
Matplotlib has a comprehensive and powerful API that allows you to change almost any attribute of the graph to your liking. The combination of seaborn's advanced interface and matplotlib's deep customizability allows Seaborn to both quickly explore data and create customizable publications of quality. Graphics of the final product.
3. Pyecharts: interactive charts
Official website: https://pyecharts.org/#/
Echarts is a data visualization open sourced by Baidu. It has been recognized by many developers for its good interactivity and exquisite chart design. Python is an expressive language and is very suitable for data processing. When data analysis meets data visualization, pyecharts was born.
Pyecharts has a simple API design, is as smooth as silk, supports chain calls, includes 30+ common charts, everything you need, supports mainstream Notebook environments, Jupyter Notebook and JupyterLab, and has highly flexible configuration items that can be easily matched Beautiful chart.
The powerful data interaction function of Pyecharts makes data expression information more vivid and increases the human-computer interaction effect. The data presentation effect can be directly exported to html files, increasing the opportunity for data result interaction and making information communication easier.
Pyecharts has rich chart materials and supports chain calls. The following is a geographical chart function using Pyecharts to intuitively display data visualization effects in space.
4. wordcloud: word cloud chart
To draw a word cloud chart, you can use the wordcloud library in Python. First, use pip install wordcloud to install the library. After importing the text data, create a WordCloud object, set the background color, width, and height of the word cloud chart, and use the generate() method to convert the text Pass it to the word cloud object to generate the word cloud graph. Finally, use the imshow() method to display the word cloud graph, and use the axis() method to hide the coordinate axis.
import matplotlib.pyplot as plt
from wordcloud import WordCloud
text = "This is some sample text for generating a word cloud."
# 创建词云对象
wordcloud = WordCloud(background_color='white', width=800, height=600).generate(text)
# 显示词云图
plt.figure(figsize=(9, 6))
plt.imshow(wordcloud, interpolation='bilinear')
plt.axis("off")
plt.show()
5. Plotly: dynamic interactive charts
Official website: https://plotly.com/python/statistical-charts/
Plotly is a data visualization Python library that can draw various chart types such as line graphs, scatter plots, bar graphs, box plots, heat maps, etc. It has interactive features. It can also be created, shared and displayed directly on the web. Publish interactive graphics. Use Plotly to quickly create beautiful 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. They also support use in jupyter notebooks to facilitate data analysis. and interactive data visualization.
Plotly is a powerful and easy-to-use data visualization tool. It is relatively easy to get started. There are many chart cases on its official website that can be used directly. It greatly facilitates readers to do data analysis and is suitable for use by data analysts.
The above are the commonly used data visualization libraries summarized by the author. Can they be used as data visualization gadgets? If you want to learn more Python data visualization libraries,
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