search
HomeBackend DevelopmentPython TutorialShare a case of how Python uses plotly to draw data charts (pictures and texts)

This article mainly introduces the method of using plotly to draw data charts in Python. It analyzes the techniques of plotly drawing with examples and has certain reference value. Interested friends can refer to it

Introduction: Use The python-plotly module is used to draw stress test data and generate static html page results for display.

Many friends have the experience of stress testing modules during the development process. After the stress test, everyone often likes to use Excel to process the stress test data and draw data visualization views, but this is not very convenient to use. Web page for data display. This article will introduce the use of the python-plotly module to draw stress test data and generate a static HTML page to facilitate the display of results.

Introduction to Plotly

Plotly is a charting tool developed using JavaScript, providing an API to interact with mainstream data analysis languages ​​(such as: Python, R, MATLAB). You can go to the official website https://plot.ly/ for more detailed information. Plotly is capable of drawing beautiful charts with user interaction.

Python-Plotly installation

This document mainly introduces the use of plotly’s Python API to perform several A simple chart drawing. For more usage of Plotly, please refer to https://plot.ly/python/

Python-Plotly can be installed using pip, and it is best to install and use it in Python version 2.7 and above. If you use Python2.6 version, please install Python2.7 and the corresponding pip yourself.

Plotly drawing example

line-plots

Drawing effect:

The generated html page provides rich interactive tools in the upper right corner.

Code:


def line_plots(name):
  '''
  绘制普通线图
  '''
  #数据,x为横坐标,y,z为纵坐标的两项指标,三个array长度相同
  dataset = {'x':[0,1,2,3,4,5,6,7,8,9],
        'y':[5,4,1,3,11,2,6,7,19,20],
        'z':[12,9,0,0,3,25,8,17,22,5]}
  data_g = []
  #分别插入 y, z
  tr_x = Scatter(
    x = dataset['x'],
    y = dataset['y'],
    name = 'y' 
  )
  data_g.append(tr_x)
  tr_z = Scatter(
    x = dataset['x'],
    y = dataset['z'],
    name = 'z' 
  )
  data_g.append(tr_z)
  #设置layout,指定图表title,x轴和y轴名称
  layout = Layout(title="line plots", xaxis={'title':'x'}, yaxis={'title':'value'})
  #将layout设置到图表
  fig = Figure(data=data_g, layout=layout)
  #绘图,输出路径为name参数指定
  pltoff.plot(fig, filename=name)

scatter-plots

Plotting effect :

Code:


def scatter_plots(name):
  '''
  绘制散点图
  '''
  dataset = {'x':[0,1,2,3,4,5,6,7,8,9],
        'y':[5,4,1,3,11,2,6,7,19,20],
        'text':['5_txt','4_txt','1_txt','3_txt','11_txt','2_txt','6_txt','7_txt','19_txt','20_txt']}

  data_g = []

  tr_x = Scatter(
    x = dataset['x'],
    y = dataset['y'],
    text = dataset['text'],
    textposition='top center',
    mode='markers+text',
    name = 'y' 
  )
  data_g.append(tr_x)

  layout = Layout(title="scatter plots", xaxis={'title':'x'}, yaxis={'title':'value'})
  fig = Figure(data=data_g, layout=layout)
  pltoff.plot(fig, filename=name)

bar-charts

Drawing Effect:

Code:


def bar_charts(name):
  '''
  绘制柱状图
  '''
  dataset = {'x':['Windows', 'Linux', 'Unix', 'MacOS'],
        'y1':[45, 26, 37, 13],
        'y2':[19, 27, 33, 21]}
  data_g = []
  tr_y1 = Bar(
    x = dataset['x'],
    y = dataset['y1'],
    name = 'v1'
  )
  data_g.append(tr_y1)

  tr_y2 = Bar(
    x = dataset['x'],
    y = dataset['y2'],
    name = 'v2'
  )
  data_g.append(tr_y2)
  layout = Layout(title="bar charts", xaxis={'title':'x'}, yaxis={'title':'value'})
  fig = Figure(data=data_g, layout=layout)
  pltoff.plot(fig, filename=name)

pie-charts

Plotting effect:

Code:


def pie_charts(name):
  '''
  绘制饼图
  '''
  dataset = {'labels':['Windows', 'Linux', 'Unix', 'MacOS', 'Android', 'iOS'],
        'values':[280, 25, 10, 100, 250, 270]} 
  data_g = []
  tr_p = Pie(
    labels = dataset['labels'],
    values = dataset['values']
  )
  data_g.append(tr_p)
  layout = Layout(title="pie charts")
  fig = Figure(data=data_g, layout=layout)
  pltoff.plot(fig, filename=name)

filled-area-plots

This example is to draw a stacked line chart with a fill effect, which is suitable for analyzing data with stacked percentage attributes

Drawing effect:

Code:


def filled_area_plots(name):
  '''
  绘制堆叠填充的线图
  '''
  dataset = {'x':[0,1,2,3,4,5,6,7,8,9],
        'y1':[5,4,1,3,11,2,6,7,19,20],
        'y2':[12,9,0,0,3,25,8,17,22,5],
        'y3':[13,22,46,1,15,4,18,11,17,20]}

  #计算y1,y2,y3的堆叠占比
  dataset['y1_stack'] = dataset['y1']
  dataset['y2_stack'] = [y1+y2 for y1, y2 in zip(dataset['y1'], dataset['y2'])]
  dataset['y3_stack'] = [y1+y2+y3 for y1, y2, y3 in zip(dataset['y1'], dataset['y2'], dataset['y3'])]

  dataset['y1_text'] = ['%s(%s%%)'%(y1, y1*100/y3_s) for y1, y3_s in zip(dataset['y1'], dataset['y3_stack'])]
  dataset['y2_text'] = ['%s(%s%%)'%(y2, y2*100/y3_s) for y2, y3_s in zip(dataset['y2'], dataset['y3_stack'])]
  dataset['y3_text'] = ['%s(%s%%)'%(y3, y3*100/y3_s) for y3, y3_s in zip(dataset['y3'], dataset['y3_stack'])]

  data_g = []
  tr_1 = Scatter(
    x = dataset['x'],
    y = dataset['y1_stack'],
    text = dataset['y1_text'],
    hoverinfo = 'x+text',
    mode = 'lines',
    name = 'y1', 
    fill = 'tozeroy' #填充方式: 到x轴
  )
  data_g.append(tr_1)

  tr_2 = Scatter(
    x = dataset['x'],
    y = dataset['y2_stack'],
    text = dataset['y2_text'],
    hoverinfo = 'x+text',
    mode = 'lines',
    name = 'y2', 
    fill = 'tonexty' #填充方式:到下方的另一条线
  )
  data_g.append(tr_2)

  tr_3 = Scatter(
    x = dataset['x'],
    y = dataset['y3_stack'],
    text = dataset['y3_text'],
    hoverinfo = 'x+text',
    mode = 'lines',
    name = 'y3',
    fill = 'tonexty'
  )
  data_g.append(tr_3)

  layout = Layout(title="field area plots", xaxis={'title':'x'}, yaxis={'title':'value'})
  fig = Figure(data=data_g, layout=layout)
  pltoff.plot(fig, filename=name)

Summary

This article introduces the method of using python-plotly to draw data graphs, including line plots and scatter plots in examples. These five typical charts, scatter plots, bar charts, pie charts, and filled area plots, basically cover most types of test data. Dear friends, It can be deformed to draw more beautiful icons.

The above is the detailed content of Share a case of how Python uses plotly to draw data charts (pictures and texts). For more information, please follow other related articles on the PHP Chinese website!

Statement
The content of this article is voluntarily contributed by netizens, and the copyright belongs to the original author. This site does not assume corresponding legal responsibility. If you find any content suspected of plagiarism or infringement, please contact admin@php.cn
Python vs. C  : Understanding the Key DifferencesPython vs. C : Understanding the Key DifferencesApr 21, 2025 am 12:18 AM

Python and C each have their own advantages, and the choice should be based on project requirements. 1) Python is suitable for rapid development and data processing due to its concise syntax and dynamic typing. 2)C is suitable for high performance and system programming due to its static typing and manual memory management.

Python vs. C  : Which Language to Choose for Your Project?Python vs. C : Which Language to Choose for Your Project?Apr 21, 2025 am 12:17 AM

Choosing Python or C depends on project requirements: 1) If you need rapid development, data processing and prototype design, choose Python; 2) If you need high performance, low latency and close hardware control, choose C.

Reaching Your Python Goals: The Power of 2 Hours DailyReaching Your Python Goals: The Power of 2 Hours DailyApr 20, 2025 am 12:21 AM

By investing 2 hours of Python learning every day, you can effectively improve your programming skills. 1. Learn new knowledge: read documents or watch tutorials. 2. Practice: Write code and complete exercises. 3. Review: Consolidate the content you have learned. 4. Project practice: Apply what you have learned in actual projects. Such a structured learning plan can help you systematically master Python and achieve career goals.

Maximizing 2 Hours: Effective Python Learning StrategiesMaximizing 2 Hours: Effective Python Learning StrategiesApr 20, 2025 am 12:20 AM

Methods to learn Python efficiently within two hours include: 1. Review the basic knowledge and ensure that you are familiar with Python installation and basic syntax; 2. Understand the core concepts of Python, such as variables, lists, functions, etc.; 3. Master basic and advanced usage by using examples; 4. Learn common errors and debugging techniques; 5. Apply performance optimization and best practices, such as using list comprehensions and following the PEP8 style guide.

Choosing Between Python and C  : The Right Language for YouChoosing Between Python and C : The Right Language for YouApr 20, 2025 am 12:20 AM

Python is suitable for beginners and data science, and C is suitable for system programming and game development. 1. Python is simple and easy to use, suitable for data science and web development. 2.C provides high performance and control, suitable for game development and system programming. The choice should be based on project needs and personal interests.

Python vs. C  : A Comparative Analysis of Programming LanguagesPython vs. C : A Comparative Analysis of Programming LanguagesApr 20, 2025 am 12:14 AM

Python is more suitable for data science and rapid development, while C is more suitable for high performance and system programming. 1. Python syntax is concise and easy to learn, suitable for data processing and scientific computing. 2.C has complex syntax but excellent performance and is often used in game development and system programming.

2 Hours a Day: The Potential of Python Learning2 Hours a Day: The Potential of Python LearningApr 20, 2025 am 12:14 AM

It is feasible to invest two hours a day to learn Python. 1. Learn new knowledge: Learn new concepts in one hour, such as lists and dictionaries. 2. Practice and exercises: Use one hour to perform programming exercises, such as writing small programs. Through reasonable planning and perseverance, you can master the core concepts of Python in a short time.

Python vs. C  : Learning Curves and Ease of UsePython vs. C : Learning Curves and Ease of UseApr 19, 2025 am 12:20 AM

Python is easier to learn and use, while C is more powerful but complex. 1. Python syntax is concise and suitable for beginners. Dynamic typing and automatic memory management make it easy to use, but may cause runtime errors. 2.C provides low-level control and advanced features, suitable for high-performance applications, but has a high learning threshold and requires manual memory and type safety management.

See all articles

Hot AI Tools

Undresser.AI Undress

Undresser.AI Undress

AI-powered app for creating realistic nude photos

AI Clothes Remover

AI Clothes Remover

Online AI tool for removing clothes from photos.

Undress AI Tool

Undress AI Tool

Undress images for free

Clothoff.io

Clothoff.io

AI clothes remover

Video Face Swap

Video Face Swap

Swap faces in any video effortlessly with our completely free AI face swap tool!

Hot Tools

SublimeText3 English version

SublimeText3 English version

Recommended: Win version, supports code prompts!

mPDF

mPDF

mPDF is a PHP library that can generate PDF files from UTF-8 encoded HTML. The original author, Ian Back, wrote mPDF to output PDF files "on the fly" from his website and handle different languages. It is slower than original scripts like HTML2FPDF and produces larger files when using Unicode fonts, but supports CSS styles etc. and has a lot of enhancements. Supports almost all languages, including RTL (Arabic and Hebrew) and CJK (Chinese, Japanese and Korean). Supports nested block-level elements (such as P, DIV),

SublimeText3 Mac version

SublimeText3 Mac version

God-level code editing software (SublimeText3)

MinGW - Minimalist GNU for Windows

MinGW - Minimalist GNU for Windows

This project is in the process of being migrated to osdn.net/projects/mingw, you can continue to follow us there. MinGW: A native Windows port of the GNU Compiler Collection (GCC), freely distributable import libraries and header files for building native Windows applications; includes extensions to the MSVC runtime to support C99 functionality. All MinGW software can run on 64-bit Windows platforms.

Atom editor mac version download

Atom editor mac version download

The most popular open source editor