preface
In the Internet era, a lot of data will be generated in the network every day. After data analysis, we need to visually display the data to better interpret the meaning behind the data.
In data visualization, Python also supports third modules.
- matplotlib module: the most used visualization Library in Python
- seaborn module: graphic visualization based on matplotlib
- pycharts module: a class library used to generate Echarts charts
In this issue, we learn the graphic methods provided by the matplotlib module, Let's go~
1. Overview of Matplotlib module
matplotlib module is a third-party open source module developed by John Hunter team and sponsored by NumFOCUS.
The matplotlib module is a comprehensive library for Python to create static, dynamic and interactive visualization.
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matplotlib module features
- Easy to create charts, such as publishing quality chart and interactive data, which can be enlarged and reduced
- Customized charts can fully control line styles, import and embed a variety of file formats
- High scalability, compatible with third-party modules
- matplotlib module material manual is rich in information and can be used quickly
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matplotlib module acquisition
matplotlib is the mainstream third-party visualization module in Python. We need to download it using pip
pip install matplotlib Copy code
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The matplotlib module uses
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In the matplotlib module, the pyplot class is the most commonly used.- Mode 1:
from matplotlib import pyplot Copy code
- Mode 2:
import matplotlib.pyplot as plt Copy code
🔔 Important note
- matplotlib module official information
- See matplotlib internal code description
matplotlib.pyplot related methods
matplotlib.pyplot Module is one of the most commonly used modules for drawing icons
method | effect |
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pyplot.title(name) | Title of chart |
pyplot.xlabel(name) | X axis name of the chart |
pyplot.ylabel(name) | y-axis name of the chart |
pyplot.show() | Print out 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) | Scatter plot |
pyplot.subplot(data) | Draw subgraph |
pyplot.grid(boolean) | Display mesh, the default is False |
pyplot.text() | Process text |
pyplot.pie(data) | Draw pie chart |
pyplot.boxplot(data) | Draw box diagram |
pyplot.hist(data) | Draw histogram |
3. matplotlib.pyplot chart display
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Draw a line chart
- Use the 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 histogram
- Use the pyplot..bar() method
- Using the above data again, you can see the histogram
pyplot.bar([1,2,3,4,5,6],[45,20,19,56,35,69]) Copy code
Draw pie chart
- Use the pyplot.pie() method to draw a pie chart
- At the same time, use the pyplot.axis method to set the interval of each partition
Draw pie chart
use pyplot.pie () method to draw a pie chart, and use 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()
Scatter plot
- Use pyplot.scatter(x,y) to 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("element a") pyplot.ylabel("element b") pyplot.show()
summary
In this issue, we will simply learn about the matplotlib.pyplot module drawing related charts such as broken line, column, scatter point and round cake
In the process of learning, we found that the pyplot module is easy to use, and all our data is the key point before presentation
The above is the content of this issue. Welcome to praise and comment. I'll see you next time~
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