import matplotlib.pyplot as plt

import numpy as np

n = 100

X = np.random.random(size=n)

Y = np.random.random(size=n)

plt.scatter(X,Y,s = 5,c="b")

plt.xlim((0,1))

plt.ylim((0,1))

plt.show()

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点数

X坐标

Y坐标

散点图

横坐标范围

纵坐标范围

展示

import numpy as np

import matplotlib.pyplot as plt

x = np.arange(-10,10,0.1)

y1 = 0.05 * x ** 2

plt.plot(x,y1,'g-')

plt.show()

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X坐标

Y坐标

曲线图

展示

import matplotlib.pyplot as plt

import numpy as np

n = 12

X = [chr(i) for i in range(65,65+n)]

Y = np.random.uniform(0.5,1.0,n)

plt.bar(X,Y)

for x,y in zip(X,Y):

plt.text(x,y,'%.2f'%y,ha = 'center',va='bottom')

plt.show()

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X坐标

Y坐标

直方图

标记取值

展示

import matplotlib.pyplot as plt

import numpy as np

n = 12

X = [chr(i) for i in range(65,65+n)]

Y = np.random.uniform(0.5,1.0,n)

X.insert(1,"Z")

Y=np.insert(Y,1,0)

plt.bar(X,Y)

for x,y in zip(X,Y):

plt.text(x,y,'%.2f'%y,ha = 'center',va='bottom')

plt.show()

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X坐标

Y坐标

增加Z

增加Z的值

直方图

标记取值

展示

import matplotlib.pyplot as plt

import numpy as np

import pandas as pd

file="Hg_CData.csv"

data=pd.read_csv(file)

data_p=data.pivot(index="Subject",columns="Gender",values="Score")

labels = data_p.index

men_means = data_p.MenS

women_means = data_p.WomenS

x = np.arange(len(labels))

width = 0.35

fig, ax = plt.subplots()

rects1 = ax.bar(x - width/2, men_means, width, label='Men')

rects2 = ax.bar(x + width/2, women_means, width, label='Women')

ax.set_ylabel('Scores')

ax.set_title('Scores by group and gender')

ax.set_xticks(x)

ax.set_xticklabels(labels)

ax.legend()

plt.show()

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读数据

转换数据

科目

男生得分

女生得分

直方图宽度

男生得分直方图

女生得分直方图

设置y轴名

设置图名

设置x轴

设置x轴的科目名

图例

展示

import matplotlib.pyplot as plt

import numpy as np

import pandas as pd

file="Hg_CData.csv"

data=pd.read_csv(file)

data_p=data.pivot(index="Subject",columns="Gender",values="Score")

labels = data_p.index

men_means = data_p.MenS

women_means = data_p.WomenS

x = np.arange(len(labels))

width = 0.35

p1 = plt.bar(x, men_means, width)

p2 = plt.bar(x, women_means, width, bottom=men_means)

plt.ylabel('Scores')

plt.title('Scores by group and gender')

plt.xticks(x,labels)

plt.legend((p1[0], p2[0]), ('Men', 'Women'))

plt.show()

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读数据

转换数据

科目

男生得分

女生得分

直方图宽度

男生得分直方图

女生得分直方图

设置y轴名

设置图名

设置x轴

图例

展示

importmatplotlib.pyplotasplt

s=["a","b","c","d"]

p=[15,30,45,10]

plt.pie(p,autopct='%1.1f%%',labels=s)

plt.show()

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类别

百分比

饼图

展示

importmatplotlib.pyplotasplt

importnumpyasnp

years=["2019","2020","2021"]

s=["a","b","c","d"]

p2019=[15,30,45,10]

p2020=[23,28,32,17]

p2021=[18,21,51,10]

radius=[0.5,0.8,1]

colors=["red","orange","yellow","gray"]

angles=[30,150,270]

fig,ax=plt.subplots()

ax.pie(p2021,pctdistance=0.9,radius=radius[2],autopct='%3.1f%%',colors=colors,

wedgeprops={"edgecolor":"black"})

ax.pie(p2020,pctdistance=0.75,radius=radius[1],autopct='%3.1f%%',

colors=colors,wedgeprops={"edgecolor":"black"})

ax.pie(p2019,pctdistance=0.5,radius=radius[0],autopct='%3.1f%%',

colors=colors,wedgeprops={"edgecolor":"black"})

bbox_props=dict(boxstyle="square,pad=0.3",fc="w",ec="k",lw=0.72)

kw=dict(arrowprops=dict(arrowstyle="-"),

bbox=bbox_props,va="center")

foriinrange(len(years)):

ang=angles[i]

y=np.sin(np.deg2rad(ang))*radius[i]

x=np.cos(np.deg2rad(ang))*radius[i]

ha={-1:"right",1:"left"}[int(np.sign(x))]

connectionstyle="angle,angleA=0,angleB={}".format(ang)

kw["arrowprops"].update({"connectionstyle":connectionstyle})

plt.annotate(years[i],xy=(x,y),xytext=(1.35*np.sign(x),1.4*y),

ha=ha,**kw,fontsize=18,weight='bold')

plt.legend(s,bbox_to_anchor=(1.05,1),loc='best',borderaxespad=0.)

plt.show()

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年份

类别

2019年类别占比

2020年类别占比

2021年类别占比

嵌套饼图半径

颜色

标记年份的角度

画布

画2021年饼图

画2020年饼图

画2019年饼图

标注线设置

标注年份

标注的纵坐标

标注的横坐标

⽔平对⻬⽅式

箭头连接样式

更新箭头连接⽅式

标注年份

图例

import matplotlib.pyplot as plt

import numpy as np

x = np.arange(0,10,0.1)

y1 = 0.05 * x ** 2

y2 = -1 * y1

fig,ax1 = plt.subplots()

ax2 = ax1.twinx()

ax1.plot(x,y1,'g-')

ax2.plot(x,y2,'b-')

ax1.set_xlabel('X data')

ax1.set_ylabel('Y1 data',color = 'g')

ax2.set_ylabel('Y2 data',color = 'b')

plt.show()

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x

y1

y2

绘制子图

添加y2轴

绘制y1曲线

绘制y2曲线

添加x轴标签

添加y1轴标签

添加y2轴标签

展示

import pandas as pd

import numpy as np

import matplotlib.pyplot as plt

import seaborn as sns

from mpl_toolkits.mplot3d import Axes3D

plt.figure(figsize = (12,10))

ax1 = plt.subplot(2,2,1)

#绘制散点图

n = 1024

X = np.random.normal(0,1,n)

Y = np.random.normal(0,1,n)

plt.scatter(X,Y,s = 10,c="r",alpha = .2)

plt.xlim((-4,4))

plt.ylim((-4,4))

#绘制曲线图

ax3 = plt.subplot(2,2,2)

x = np.arange(-10,10,0.1)

y1 = 0.05 * x ** 2

plt.plot(x,y1,'g-')

#绘制直方图

ax3 = plt.subplot(2,2,3)

n = 12

X = [chr(i) for i in range(65,65+n)]

Y = np.random.uniform(0.5,1.0,n)

plt.bar(X,Y)

for x,y in zip(X,Y):

plt.text(x,y,'%.2f'%y,ha = 'center',va='bottom')

#绘制饼图

ax4 = plt.subplot(2,2,4)

s = ["a","b","c","d"]

p = [15, 30, 45, 10]

plt.pie(p,autopct='%1.1f%%',labels=s)

plt.show()

import numpy as np

import matplotlib.pyplot as plt

from matplotlib.animation import FuncAnimation

fig, ax = plt.subplots()

xdata, ydata = [], []

ln, = plt.plot([], [], 'ro',markersize=2)

x=np.linspace(0, 2 * np.pi, 128)

def init():

ax.set_xlim(0, 2 * np.pi)

ax.set_ylim(-1, 1)

return ln,

def update(frame):

xdata.append(frame)

ydata.append(np.sin(frame))

ln.set_data(xdata, ydata)

return ln,

ani = FuncAnimation(fig, update, frames=x,interval=10,

init_func=init, blit=True)

plt.show()

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x

初始化

更新

作图

展示

import matplotlib.pyplot as plt

fig = plt.figure()

x = [1,2,3,4,5,6,7]

y = [1,3,4,2,5,8,6]

#主图位置

left, bottom, width, height = 0.1, 0.1, 0.8, 0.8

#绘制主图

ax1 = fig.add_axes([left, bottom, width, height])# main axes

ax1.plot(x,y,'r')

ax1.set_xlabel('x')

ax1.set_ylabel('y')

ax1.set_title('title')

#绘制图中图1

ax2 = fig.add_axes([0.2, 0.6, 0.25, 0.25]) # inside axes

ax2.plot(y, x, 'b')

ax2.set_xlabel('x')

ax2.set_ylabel('y')

ax2.set_title('title inside 1')

#另一种方法绘制图中图2

plt.axes([0.6, 0.2, 0.25, 0.25])

plt.plot(y[::-1], x, 'g')

plt.xlabel('x')

plt.ylabel('y')

plt.title('title inside 2')

plt.show()

import seaborn as sns

import pandas as pd

import numpy as np

import matplotlib.pyplot as plt

df = pd.DataFrame(np.random.random((10,10)),

columns=["a","b","c","d","e","f","g","h","i","j"])

sns.heatmap(df, annot=True, annot_kws={"size": 7},cmap="coolwarm")

plt.show()

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Dataframe

绘制热图

展示

import matplotlib.pyplot as plt

import numpy as np

def f(x,y):

return (1-x/2 + x**5+y**3) * np.exp(-x **2 -y**2)

n = 256

x = np.linspace(-3,3,n)

y = np.linspace(-3,3,n)

X,Y = np.meshgrid(x,y)

Ct=f(X,Y)

C = plt.contour(X,Y,Ct,2,colors = 'black')

plt.clabel(C,inline = True,fontsize = 10)

plt.contourf(X,Y,Ct,2,alpha = .75,cmap = plt.cm.hot)

plt.show()

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计算高度值展示

X

Y

等高线网格状数据

x,y坐标对应的高度值

等高线轮廓

等高线标记

等高线填充

展示

import numpy as np

import matplotlib.pyplot as plt

from mpl_toolkits.mplot3d import Axes3D

def f(x,y):

return (1-x/2 + x**5+y**3) * np.exp(-x **2 -y**2)

fig = plt.figure()

ax = Axes3D(fig)

n=256

X = np.linspace(-3,3,n)

Y = np.linspace(-3,3,n)

X,Y = np.meshgrid(X,Y)

Z=f(X,Y)

ax.plot_surface(X, Y, Z, rstride=1, cstride=1, cmap=plt.get_cmap('rainbow'))

plt.show()

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计算Z

X

Y

网格化

Z

绘制3D图

展示

A

B

A

B

A

B

A

B

A

B

A

B

A

B

A

B

A

B

A

B

A

B