一、matplotlib库的作用与环境配置

matplotlib库是数据分析中，数据可视化的一个重要工具。

1、环境配置示例

import matplotlib.pyplot as plt

x = [1,2,3,4]
y = [1,4,9,16]

plt.plot(x,y)   # 绘制折线图
plt.ylabel("squares")   # 设置y轴标签
plt.show()  # 显示图片

2、改变绘图风格

print(plt.style.available[:])   # 显示可用的绘图风格

with plt.style.context("seaborn-white"):    # 临时改变绘图风格
    plt.show()

import matplotlib.pyplot as plt
plt.style.use("seaborn-white")  # 永久改变绘图风格

x = [1,2,3,4]
y = [1,4,9,16]

plt.plot(x,y)   # 绘制折线图
plt.ylabel("squares")   # 设置y轴标签
plt.show()  # 显示图片

3、保存图片

import matplotlib.pyplot as plt
import numpy as np

x = np.linspace(0,10,100)
plt.plot(x,np.exp(x))

plt.savefig("myfigure.png") # 保存图片

二、绘制二维图形

1、折线图

（1）示例

import matplotlib.pyplot as plt
import numpy as np
plt.style.use("seaborn-white")

x = np.linspace(0,2*np.pi,100)
plt.plot(x,np.sin(x))   # 绘制正弦曲线
plt.plot(x,np.cos(x))   # 绘制余弦曲线

plt.show()

（2）调整线条颜色：

import matplotlib.pyplot as plt
import numpy as np
plt.style.use("seaborn-white")

x = np.linspace(0,2*np.pi,100)
plt.plot(x,np.sin(x))   # 绘制正弦曲线

offsets = np.linspace(0,np.pi,5)
colors = ["blue","g","r","yellow","pink"]

for offset,color in zip(offsets,colors):
    plt.plot(x,np.sin(x-offset),color=color) # 关键字color可缩写为c

plt.show()

（3）调整线条风格

import matplotlib.pyplot as plt
import numpy as np

x = np.linspace(0,10,11)
offsets = list(range(8))
linestyles = ["solid","dashed","dashdot","dotted","-","--","-.",":"]
for offset,linestyle in zip(offsets,linestyles):
    plt.plot(x,x+offset,linestyle=linestyle)    # linestyle可缩写为ls

plt.show()

（4）调整线宽

import matplotlib.pyplot as plt
import numpy as np

x = np.linspace(0,10,11)
offsets = range(0,12,3)
linewidths = (i*2 for i in range(1,5))
for offset,linewidth in zip(offsets,linewidths):
    plt.plot(x,x+offset,linewidth=linewidth)    # linewidth可缩写为lw

plt.show()

（5）调整数据点标记

import matplotlib.pyplot as plt
import numpy as np

x = np.linspace(0,10,11)
offsets = range(0,12,3)
markers = ["*","+","o","s"]
for offset,marker in zip(offsets,markers):
    plt.plot(x,x+offset,marker=marker,markersize=10)    # markersize可缩写为ms

plt.show()

（6）颜色和风格设置的简写

import matplotlib.pyplot as plt
import numpy as np

x = np.linspace(0,10,11)
offsets = range(0,8,2)
color_linestyles = ["g-","c--","k-.","r:"]
for offset,color_linestyle in zip(offsets,color_linestyles):
    plt.plot(x,x+offset,color_linestyle)

plt.show()

import matplotlib.pyplot as plt
import numpy as np

x = np.linspace(0,10,11)
offsets = range(0,8,2)
cmls = ["g*-","b+--","ko-.","rs:"]
for offset,cml in zip(offsets,cmls):
    plt.plot(x,x+offset,cml)

plt.show()

其他用法及颜色缩写、数据点标记缩写等，请查看官方文档，如下：
https://matplotlib.org/stable/api/_as_gen/matplotlib.pyplot.plot.html

（7）调整坐标轴

x轴、y轴的边界

x = np.linspace(0,2*np.pi,100)
plt.plot(x,np.sin(x))
plt.xlim(-1,7)
plt.ylim(-1.5,1.5)
plt.show()

x = np.linspace(0,2*np.pi,100)
plt.plot(x,np.sin(x))
plt.axis([-2,8,-2,2])   #设置x轴的边界为-2到8，y轴为-2到2
plt.show()

x = np.linspace(0,2*np.pi,100)
plt.plot(x,np.sin(x))
plt.axis("tight")  
plt.show()

x = np.linspace(0,2*np.pi,100)
plt.plot(x,np.sin(x))
plt.axis("equal")
plt.show()

help(plt.axis) 可以查询 plt.axis() 中还可以设置哪些字符串参数。

对数坐标

import matplotlib.pyplot as plt
import numpy as np
import math
x = np.logspace(0,5,6)  #10**0到10**5的等比数列，取6个数


plt.plot(x,np.log(x),marker="o") # np.log(x)表示以e为底，求x的对数
plt.xscale("log")   # 表示x轴的刻度设置为对数刻度
plt.show()

调整坐标轴刻度

import matplotlib.pyplot as plt
import numpy as np

x = np.linspace(0,10,100)
plt.plot(x,x**2)
plt.xticks(np.arange(0,12,step=1),fontsize=15)
plt.yticks(np.arange(0,100,step=10))

plt.show()

调整刻度样式

import matplotlib.pyplot as plt
import numpy as np

x = np.linspace(0,10,100)
plt.plot(x,x**2)
plt.tick_params(axis="both",labelsize=15)

plt.show()

（8）设置图形标签

import matplotlib.pyplot as plt
import numpy as np

x = np.linspace(0,2*np.pi,100)
plt.plot(x,np.sin(x))
plt.title("A Sine Curve",fontsize=20)
plt.xlabel("x",fontsize=15)
plt.ylabel("sin(x)",fontsize=15)

plt.show()

（9）设置图例

默认：

import matplotlib.pyplot as plt
import numpy as np

x = np.linspace(0,2*np.pi,100)
plt.plot(x,np.sin(x),"b-",label="Sin")
plt.plot(x,np.cos(x),"r--",label="Cos")
plt.legend()
plt.show()

修饰图例：

import matplotlib.pyplot as plt
import numpy as np

x = np.linspace(0,2*np.pi,100)
plt.plot(x,np.sin(x),"b-",label="Sin")
plt.plot(x,np.cos(x),"r--",label="Cos")
plt.legend(loc="upper center",frameon=False,fontsize=15)
#loc supported values are 'best', 'upper right', 'upper left', 'lower left', 'lower right', 'right', 'center left', 'center right', 'lower center', 'upper center', 'center'
plt.ylim(-1.5,2)
plt.show()

（10）添加文字和箭头

添加文字

x = np.linspace(0,2*np.pi,100)
plt.plot(x,np.sin(x),"b-")
plt.text(3.5,0.5,"y=sin(x)",fontsize=15) # 3.5,0.5为文字所在坐标
plt.show()

添加箭头

x = np.linspace(0,2*np.pi,100)
plt.plot(x,np.sin(x),"b-")
plt.annotate("local min",xy=(1.5*np.pi,-1),xytext=(4.5,0),arrowprops=dict(facecolor="black",shrink=0.1))
# xy=(1.5*np.pi,-1) 指定了箭头指向的点的坐标。xytext=(4.5,0) 指定了注释文本的位置。arrowprops=dict(facecolor="black",shrink=0.1) 则设置了箭头的样式，包括颜色和箭头的缩放。
plt.show()

2、散点图

（1）示例

x = np.linspace(0,2*np.pi,20)
plt.scatter(x,np.sin(x),marker="s",s=30,c="r")  # s：大小 c：颜色

plt.show()

（2）颜色设置

x = np.linspace(0, 10, 10)
y = x ** 2
plt.scatter(x, y, c=y, cmap="Blues")
# c=y：散点的颜色根据y值的大小而变化
plt.colorbar()
plt.show()

颜色配置参考官方文档：
https://matplotlib.org/2.0.2/examples/color/colormaps_reference.html

（3）根据数据控制点的大小

x,y,colors,size = (np.random.rand(100) for i in range(4))
plt.scatter(x,y,c=colors,s=1000*size,cmap="viridis")

plt.colorbar()
plt.show()

（4）透明度

x,y,colors,size = (np.random.rand(100) for i in range(4))
plt.scatter(x,y,c=colors,s=1000*size,cmap="viridis",alpha=0.3)

plt.colorbar()
plt.show()

3、柱形图

（1）简单柱形图

import numpy as np
import matplotlib.pyplot as plt

x = np.arange(1,6)
plt.bar(x,2*x,align="center",width=0.5,alpha=0.5,color="yellow",edgecolor="red")
plt.xticks(x,("G1","G2","G3","G4","G5"))
plt.tick_params(axis="both",labelsize=13)

plt.show()

import numpy as np
import matplotlib.pyplot as plt

x = ["G"+str(i) for i in range(5)]
y = 1/(1+np.exp(-np.arange(5)))
colors = ["red","yellow","blue","green","gray"]
plt.bar(x,y,align="center",width=0.5,alpha=0.5,color=colors)
plt.tick_params(axis="both",labelsize=13)

plt.show()

（2）累加柱形图

import numpy as np
import matplotlib.pyplot as plt

x= np.arange(5)
y1 = np.random.randint(20,30,size=5)
y2 = np.random.randint(20,30,size=5)
plt.bar(x,y1,width=0.5,label
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