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Analysis of Matplotlib module of Python visualization

2022-01-31 06:08:50 Little cute in the circle of friends

This is my participation 11 The fourth of the yuegengwen challenge 8 God , Check out the activity details :2021 One last more challenge

Preface

In the age of Internet , In the network, a lot of data will be generated every day , After analyzing the data , How to better interpret the meaning behind the data , We need to visualize the data .

In data visualization ,Python Also supports third modules.

  • matplotlib modular :Python Most used visualization Libraries
  • seaborn modular : be based on matplotlib Graphic visualization of
  • pycharts modular : Used to generate Echarts Class library for diagrams

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In this issue , We are right. matplotlib Module provides graphical methods for learning ,Let's go~

1. matplotlib The module overview

matplotlib Modules are third-party open source , from John Hunter Developed by the team ,NumFOCUS Sponsored projects .

matplotlib The module is for Python Create static 、 Dynamic and interactive visual integrated library .

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  • matplotlib Module features

    • Easy to create charts, such as publishing quality charts 、 Interactive data can be enlarged 、 narrow
    • Custom charts have complete control over line styles 、 Import and embed multiple file formats
    • High expansibility , Compatible with third-party modules
    • matplotlib The module reference manual is informative , But get started quickly
  • matplotlib Module acquisition

    matplotlib yes Python Mainstream third-party visualization module , We need to use pip Download

    pip install matplotlib
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  • matplotlib Module USES

    stay matplotlib Module ,pyplot Class is the most commonly used .

    • Mode one :
    from matplotlib import pyplot
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    • Mode two :
    import matplotlib.pyplot as plt
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Important note

  1. matplotlib Module official information
  2. see matplotlib Internal code description

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2. matplotlib.pyplot Related methods

matplotlib.pyplot Module is one of the most commonly used modules for drawing icons

Method effect
pyplot.title(name) The title of the chart
pyplot.xlabel(name) Chart's X Axis name
pyplot.ylabel(name) Chart's y Axis name
pyplot.show() Print out the chart
pyplot.plot(xvalue,yvalue) Draw a line chart
pyplot.bar(xvalue,yvalue) Draw a column chart
pyplot.axis(data) A convenient way to get or set some axis properties
pyplot.scatter(data) Draw a scatter plot
pyplot.subplot(data) Draw a subgraph
pyplot.grid(boolean) Display mesh , The default is False
pyplot.text() Process the text
pyplot.pie(data) Draw the pie chart
pyplot.boxplot(data) Drawing box diagram
pyplot.hist(data) Draw histogram

3. matplotlib.pyplot Chart display

  • Draw line chart

    • Use pyplot..plot() Method
    from matplotlib import pyplot
    
    #  Format chart font 
    pyplot.rcParams["font.sans-serif"]=['SimHei']
    pyplot.rcParams["axes.unicode_minus"]=False
    
    pyplot.plot([1,2,3,4,5,6],[45,20,19,56,35,69])
    
    pyplot.title("data analyze")
    pyplot.xlabel("data")
    pyplot.ylabel("sum")
    
    pyplot.show()
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  • Draw a histogram

    • Use pyplot..bar() Method
    • Use the above data again , You can see the histogram
    pyplot.bar([1,2,3,4,5,6],[45,20,19,56,35,69])
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  • Draw the pie chart

    • Use pyplot.pie() Method to draw a pie chart
    • Use at the same time pyplot.axis Method to set the interval of each partition
    from matplotlib import pyplot
    labels = ["windows","MAC","ios","Android","other"]
    sizes = [50,10,5,15,20]
    explode = [0,0.1,0,0,0]
    pyplot.pie(sizes,explode=explode,labels=labels,autopct='%1.1f%%',shadow=False,startangle=90)
    pyplot.axis("equal")
    
    pyplot.title("data analyze")
    pyplot.show()
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  • Draw a scatter plot

    • Use pyplot.scatter(x,y) Draw a scatter plot
    import numpy as np
    from matplotlib import pyplot
    
    data = {"a":np.arange(50),"c":np.random.randint(0,50,50),"d":np.random.randn(50)}
    
    data['b'] = data['a']+10*np.random.randn(50)
    data['d'] = np.abs(data['d'])*100
    
    pyplot.scatter("a","b",c='c',s='d',data=data)
    
    pyplot.title("data analyze")
    pyplot.xlabel(" Elements  a")
    pyplot.ylabel(" Elements  b")
    
    pyplot.show()
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summary

In this issue , We are right. matplotlib.pyplot Draw relevant modules, such as broken lines 、 Columnar 、 Scatter 、 Simply learn the pie chart

In the process of learning , We found that pyplot The module is easy to use , It is found that all our data is the key point before presentation

The above is the content of this issue , Welcome big guys to praise and comment , See you next time ~

copyright notice
author[Little cute in the circle of friends],Please bring the original link to reprint, thank you.
https://en.pythonmana.com/2022/01/202201310608478176.html

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