Python visual data analysis 10. Matplotlib Library

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Python Visual data analysis 10、Matplotlib library

Preface

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Python Initial environment address ：【Python Visual data analysis 01、python Environment building 】 

Environmental requirements

Environmental Science ：win10

development tool ：PyCharm Community Edition 2021.2

database ：MySQL5.6

Catalog

Python Visual data analysis 10、Matplotlib library

Preface

Environmental requirements

Pre environment

Preface

Draw a straight line

plot function

Draw a histogram

Stacked histogram

Draw a side-by-side histogram

Draw histogram

Draw the pie chart

Draw a split pie chart

Draw a scatter plot

draw 3D Images

3D Surface graph

3D Scatter plot

3D Bar chart

Pre environment

pip3 config set global.index-url https://repo.huaweicloud.com/repository/pypi/simplepip3 config listpip3 install --upgrade pippip3 install numpypip3 install matplotlib

​ edit Things are relatively large, and the introduction is slower , Don't worry. .

Preface

Matplotlib yes Python One of the most commonly used visualization tools in , It is very convenient to create massive 2D Charts and some basic 3D Chart .
Matplotlib First published in 2007 year , Driven by open source and the community , Now based on Python It has been widely used in various fields of scientific computing .
Matplotlib The most widely used module in is pyplot modular ,pyplot Each drawing function in the module can make some changes to the graph .

Draw a straight line

import numpy as npfrom matplotlib import pyplot as pltplt.rcParams['font.sans-serif'] = ['SimHei'] # Used to display Chinese labels normally plt.rcParams['axes.unicode_minus'] = False # Used to display negative sign normally x = np.arange(1, 11)y = 2 * xplt.title(" I'm the title ")plt.xlabel(" I am a X Axis ")plt.ylabel(" I am a Y Axis ")plt.plot(x, y)plt.show()

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Irregular value

import numpy as npfrom matplotlib import pyplot as pltplt.rcParams['font.sans-serif'] = ['SimHei'] # Used to display Chinese labels normally plt.rcParams['axes.unicode_minus'] = False # Used to display negative sign normally x = np.arange(1, 11)y = 2 * xplt.title(" I'm the title ")plt.xlabel(" I am a X Axis ")plt.ylabel(" I am a Y Axis ")plt.plot((1, 5, 1, 5, 7, 5, 5, 9, 1, 5), y)plt.show()

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plot function

plot() Functions can pass in multiple parameters , Among them the first 3 Parameters represent the color and type of the line , The first 4 Parameters represent the width of the line

character

meaning

-

Solid line pattern

--

Short horizontal line pattern

-.

Dash pattern

:

Dashed pattern

.

Dot the mark

,

Pixel marker

o

Circle marks

v

Inverted triangle

^

Positive triangle sign

<

Left triangle

>

Right triangle

1

Down arrow mark

2

Up arrow mark

3

Left arrow mark

4

Right arrow mark

s

A square mark

p

Pentagonal sign

*

Star sign

h

Hexagon sign 1

'H'

Hexagon sign 2

+

Plus sign

x

X Mark

D

Diamond mark

'd'

Narrow diamond mark

|

Vertical line marking

_

Horizontal line marking

b

Blue

g

green

r

Red

c

Cyan

m

magenta

y

yellow

k

black

w

white

import numpy as npfrom matplotlib import pyplot as pltplt.rcParams['font.sans-serif'] = ['SimHei'] # Used to display Chinese labels normally plt.rcParams['axes.unicode_minus'] = False # Used to display negative sign normally x = np.arange(1, 11)y = 2 * xplt.title(" I'm the title ")plt.xlabel(" I am a X Axis ")plt.ylabel(" I am a Y Axis ")plt.plot(x, y, "^m")plt.show()

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Draw a histogram

import numpy as npimport matplotlib.pyplot as pltplt.rcParams['font.sans-serif'] = ['SimHei'] # Used to display Chinese labels normally plt.rcParams['axes.unicode_minus'] = False # Used to display negative sign normally x = ['2018 year ', '2019 year ', '2020 year ', '2021 year ', '2022 year ']y = np.random.randint(0, 100, 5)plt.bar(x, y)plt.title(" Yield change ")plt.show()

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Stacked histogram

import numpy as npimport matplotlib.pyplot as pltplt.rcParams['font.sans-serif'] = ['SimHei'] # Used to display Chinese labels normally plt.rcParams['axes.unicode_minus'] = False # Used to display negative sign normally x = ['2018 year ', '2019 year ', '2020 year ', '2021 year ', '2022 year ']y1 = np.random.randint(10, 20, 5)y2 = np.random.randint(10, 20, 5)plt.bar(x, y1)plt.bar(x, y2, bottom=y1)plt.ylabel(" yield ( Trillion tons )")plt.legend(labels=[" food ", " Wheat "], loc="upper left")plt.show()

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Draw a side-by-side histogram

import numpy as npimport matplotlib.pyplot as pltplt.rcParams['font.sans-serif'] = ['SimHei'] # Used to display Chinese labels normally plt.rcParams['axes.unicode_minus'] = False # Used to display negative sign normally x1 = np.arange(5)y1 = np.random.randint(10, 20, 5)y2 = np.random.randint(10, 20, 5)bar_width = 0.35plt.bar(x1, y1, bar_width)plt.bar(x1 + bar_width, y2, bar_width)plt.ylabel(" yield ( Trillion tons )")tick_label = ['2018 year ', '2019 year ', '2020 year ', '2021 year ', '2022 year ']plt.xticks(x1 + bar_width / 2, tick_label)plt.legend(labels=[" food ", " Wheat "], loc="upper left")plt.show()

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Draw histogram

import numpy as npimport matplotlib.pyplot as pltimport randomplt.rcParams['font.sans-serif'] = ['SimHei'] # Used to display Chinese labels normally plt.rcParams['axes.unicode_minus'] = False # Used to display negative sign normally a = [random.randint(80, 150) for i in range(250)]print(a)print(max(a) - min(a))# Count groups d = 3 # Group spacing num_bins = (max(a) - min(a)) // d# Set graphic size plt.figure(figsize=(20, 8), dpi=80)plt.hist(a, num_bins)# Set up x Axis scale plt.xticks(range(min(a), max(a) + d, d))# set grid plt.grid(alpha=0.4)plt.show()

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Draw the pie chart

import numpy as npimport matplotlib.pyplot as pltplt.rcParams['font.sans-serif'] = ['SimHei'] # Used to display Chinese labels normally plt.rcParams['axes.unicode_minus'] = False # Used to display negative sign normally labels =['2018 year ', '2019 year ', '2020 year ', '2021 year ', '2022 year ']y = np.random.rand(5)plt.pie(y, labels=labels, autopct="%3.1f%%", startangle=45, # First slice rotation angle shadow=True, pctdistance=0.8, labeldistance=1.2)plt.show()

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Draw a split pie chart

import numpy as npimport matplotlib.pyplot as pltplt.rcParams['font.sans-serif'] = ['SimHei'] # Used to display Chinese labels normally plt.rcParams['axes.unicode_minus'] = False # Used to display negative sign normally labels =['2018 year ', '2019 year ', '2020 year ', '2021 year ', '2022 year ']y = np.random.rand(5)plt.pie(y, explode=(0.1, 0.1, 0.1, 0.1, 0.1), # Percentage of edge deviation from diameter labels=labels, autopct="%3.1f%%", startangle=45, # First slice rotation angle shadow=True, pctdistance=0.8, labeldistance=1.2)plt.show()

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Draw a scatter plot

Scatter diagram is also called scatter distribution diagram , It takes a feature as the abscissa , Take another feature as ordinate , Using coordinate points （ Scatter ） The distribution form of reflects the statistical relationship between characteristics .
Scatterplot can provide two kinds of key information ：
Whether there is a numerical or quantitative correlation trend between features , Is the correlation trend linear or nonlinear
If a point or several points deviate from most points , Then these points are outliers , We can further analyze whether these outliers have a great impact on modeling analysis

import numpy as npimport matplotlib.pyplot as pltplt.rcParams['font.sans-serif'] = ['SimHei'] # Used to display Chinese labels normally plt.rcParams['axes.unicode_minus'] = False # Used to display negative sign normally x = np.arange(0, 100)y = np.random.normal(1, 20, 100)plt.scatter(x, y, label=' Coordinates ', color='k', s=25, marker="o")plt.xlabel('X Axis ')plt.ylabel('Y Axis ')plt.title(' title ')plt.legend()plt.show()

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draw 3D Images

3D Surface graph

import numpy as npimport matplotlib.pyplot as pltfrom mpl_toolkits.mplot3d import Axes3Dplt.rcParams['font.sans-serif'] = ['SimHei'] # Used to display Chinese labels normally plt.rcParams['axes.unicode_minus'] = False # Used to display negative sign normally fig = plt.figure() # Use figure object ax = Axes3D(fig) # establish 3D Axis objects X = np.arange(-2, 2, 0.1) # X Coordinate data Y = np.arange(-2, 2, 0.1) # Y Coordinate data X, Y = np.meshgrid(X, Y) # Calculation 3 Dimension surface grid coordinates # Used to calculate X/Y Corresponding Z value def f(x, y): return (1 - y ** 5 + x ** 5) * np.exp(-x ** 2 - y ** 2)# plot_surface() Function to draw the corresponding surface ax.plot_surface(X, Y, f(X, Y), rstride=1, cstride=1)plt.show() # The graphics 

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3D Scatter plot

import numpy as npimport matplotlib.pyplot as pltfrom mpl_toolkits.mplot3d import Axes3Dplt.rcParams['font.sans-serif'] = ['SimHei'] # Used to display Chinese labels normally plt.rcParams['axes.unicode_minus'] = False # Used to display negative sign normally xs = np.random.randint(30, 40, 100)ys = np.random.randint(20, 30, 100)zs = np.random.randint(10, 20, 100)xs2 = np.random.randint(50, 60, 100)ys2 = np.random.randint(30, 40, 100)zs2 = np.random.randint(50, 70, 100)xs3 = np.random.randint(10, 30, 100)ys3 = np.random.randint(40, 50, 100)zs3 = np.random.randint(40, 50, 100)fig = plt.figure()ax = Axes3D(fig)ax.scatter(xs, ys, zs)ax.scatter(xs2, ys2, zs2, c='r', marker='^')ax.scatter(xs3, ys3, zs3, c='g', marker='*')ax.set_xlabel('X label')ax.set_ylabel('Y label')ax.set_zlabel('Z label')plt.show()

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3D Bar chart

import numpy as npimport matplotlib.pyplot as pltfrom mpl_toolkits.mplot3d import Axes3Dplt.rcParams['font.sans-serif'] = ['SimHei'] # Used to display Chinese labels normally plt.rcParams['axes.unicode_minus'] = False # Used to display negative sign normally x = np.arange(8)y = np.random.randint(0, 10, 8)y2 = y + np.random.randint(0, 3, 8)y3 = y2 + np.random.randint(0, 3, 8)y4 = y3 + np.random.randint(0, 3, 8)y5 = y4 + np.random.randint(0, 3, 8)clr = ['red', 'green', 'blue', 'black', 'white', 'yellow', 'orange', 'pink']fig = plt.figure()ax = Axes3D(fig)ax.bar(x, y, 0, zdir='y', color=clr)ax.bar(x, y2, 10, zdir='y', color=clr)ax.bar(x, y3, 20, zdir='y', color=clr)ax.bar(x, y4, 30, zdir='y', color=clr)ax.bar(x, y5, 40, zdir='y', color=clr)ax.set_xlabel('X label')ax.set_ylabel('Y label')ax.set_zlabel('Z label')plt.show()

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