best python visualization library

the internal relationship of data. The interactive GUI is a good choice for interactive support.MayaviThis is a 3D graphics toolkit based on the open source C + + graphics library VTK. can be integrated into Ipython for interactive use.Other librariesOther libraries or applications include: PYQWT, Veusz, Gnuplotpy, Biggles, and so on, and large libraries are developing to web-based technologies and moving away from desktop graphics technology.The fut

Echarts Baidu is very famous also very diao.Echarts is Baidu Open source of a data visualization JS library. Mainly used for data visualization.Pyecharts is a class library that is used to generate echarts charts. is actually the butt of echarts and Python. Url:Https://github.com/chenjiandongx/pyecharts/blob/master/doc

interactive so that everyone who can access the internet can watch it in their spare time. The key to this question is how to tell a story. Each article has a different storytelling perspective, but there are clues to connect them with words. "Osama bin Laden", "Guantanamo Bay", "Freedom", and more words form the tiles of my model.Get Data None of the sources is better suited to tell the story of 911 than the New York Times. They also have a magical API that allows you to query the database f

FrontierPython provides a number of modules for data visualization, including Matplotlib, Pygal. I refer to the online popular books "Python programming from the beginning to the actual combat", in the test and learning process encountered a few problems to solve, just write down this project experience, for the basic part of the Python is not detailed, mainly th

Background: Based on the huge demand for visualizations and cost factors, using the Pyecharts + Django visualization is clearly a better choiceVisualize to find: patterns, relationships, and anomaliesEnvironment: People with obsessive-compulsive disorder have always used the latest versiondjango:2.1.0python:3.x (Win10 is 3.7,ubuntu is 3.5)Operating system environment: WIN10 and Ubuntu1. Django Installation:Django is a free, open-source web framework d

Author: vamei Source: http://www.cnblogs.com/vamei welcome reprint, please also keep this statement. Thank you! In the process of data visualization, we often need to display the data on the map based on the collected geographical location. For example, we want to map cities, aircraft routes, military bases, and so on. Generally, a Geographic Information System has such a function. Today we will discuss how to implement it in

First, the visualization method Bar chart Pie chart Box-line Diagram (box chart) Bubble chart Histogram Kernel density estimation (KDE) diagram Line Surface Chart Network Diagram Scatter chart Tree Chart Violin chart Square Chart Three-dimensional diagram Second, interactive tools Ipython, Ipython Notebook plotly Iii. Python IDE

Seaborn Library Handbook Translation Introductory Remarks: Seaborn is actually a more advanced API encapsulation based on Matplotlib, making it easier to draw and, in most cases, using Seaborn to make attractive graphs. I am here to do my best to translate it (the dog has not seen the original computer in English before). ), convenient for everyone to inquire ~ ~ ~ Detailed Introduction can see Seaborn official API and Example Gallery one, style man

Http://pbpython.com/visualization-tools-1.htmlhttps://bokeh.pydata.org/en/latest/Https://github.com/bokeh/bokehHttp://biobits.org/bokeh-jupyter-embed.htmlImport Pandas as PDFrom bokeh.plotting import figure, ShowFrom Bokeh.io import Output_notebook, output_filePower = pd.read_csv (' power.csv ')Time = power[' time '].tolist ()Current = power[' current '].tolist ()Plot = figure (title= ' Power Curve ', plot_width=1900, plot_height=500)Plot.line (X=time

segmentation situation. The red part is the process of running the program.11, continue to write code, the frequency of the statistical summary, the code implementation as shown.12, the program run, get a TXT and Excel file, inside is about the word frequency statistics information, as shown. The red part is the result of the program running, and there is no error.13. Import these keywords into WordArt for visualization, as shown in.14, set a case, f

Introduction URL: Https://www.kaggle.com/benhamner/d/uciml/iris/python-data-visualizations/notebookImport Matplotlib.pyplot as PltImport Seaborn as SNSImport Pandas as PDImport data:Iris=pd.read_csv (' E:\\data\\iris.csv ')Iris.head ()To make a histogram:Plt.hist (iris[' SEPALLENGTHCM '],bins=15)Plt.xlabel (' SEPALLENGTHCM ')Plt.ylabel (' quantity ')Plt.title (' Distribution of SEPALLENGTHCM ')Plt.show ()To make a scatter plot:But such a diagram does

‘)plt.legend(loc=‘upper right‘)plt.xticks((0,2,4,6,8,10),(‘1月‘,‘3月‘,‘5月‘,‘7月‘,‘9月‘,‘11月‘))plt.xlabel(‘月份‘)plt.ylabel(‘XX事件数‘)plt.grid(x1)plt.show()5. Read the hourly frequency data, draw the overlapping bar chartdata_hour2015 = pd.read_csv(‘data_hour2015.txt‘)data_hour2016 = pd.read_csv(‘data_hour2016.txt‘)plt.figure(figsize=(10, 6))data_hour2015[‘nums‘].T.plot.bar(color=‘g‘,alpha=0.6,label=‘2015年‘)data_hour2016[‘nums‘].T.plot.bar(color=‘r‘,alpha=0.4,label=‘2016年‘)plt.xlabel(‘小时‘)plt.ylabel(‘XX事

The Bokeh package is an interactive visual library. It is displayed using a Web browser, with the goal of drawing the pattern in d3.js style, so that it looks beautiful and easy to construct. Bokeh supports a large number of streaming datasets. You can use this library to create a variety of charts/graphs. One of its main rivals may be plotly. D3.js is a JavaScript lib