04- Matplotlib数据可视化详解 (数据库)

Matplotlib的亮点:

• import matplotlib.pyplot as plt    # 导包
• plt.figure(figsize = (9, 6) , 设置图片大小
• plt. plot(x, y), 画图
• 绘制网格线:  线型, 颜色, 透明度
• plt.grid(linestyle= '--', color= 'green',  alpha=0.75)  # linestyle: 样式, color: 颜色, alpha: 透明度
• plt.axis(), 设置坐标轴范围
• plt.xlim() , 设置X轴范围
• plt.ylim(), 设置Y轴范围
• plt.xticks(np.arange(0,7,np.pi/2))    # 设置X轴刻度
• plt.yticks([-1,0,1])    # 设置Y轴刻度
• plt.title('正弦波'), 设置中文字体:

from matplotlib.font_manager import FontManager
# fm = FontManager()
# mat_fonts = set(f.name for f in fm.ttflist)  # 遍历所有字体
# print(mat_fonts)  # 获取电脑自带字体列表
plt.rcParams['font.sans-serif'] = 'Adobe Heiti Std' # 设置宋体，显示中文
plt.title('正弦波')  # 设置标题

• plt.legend(['Sin','Cos']),  设置图例
• plt.savefig('./基础5.png', dpi = 100,  facecolor = 'violet' )  保存图片

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第一部分 基础知识

在数据分析与机器学习中，Matplotlib算得上是最常用的可视化工具。Matplotlib 是 python 最著名的绘图库，它提供了一整套 API，十分适合绘制图表，或修改图表的一些属性，如字体、标签、范围等。

Matplotlib 是一个 Python 的 2D 绘图库，它交互式环境生成出版质量级别的图形。通过 Matplotlib这个标准类库，开发者只需要几行代码就可以实现生成绘图，折线图、散点图、柱状图、饼图、直方图、组合图等数据分析可视化图表。

第一节 图形绘制

• plt.figure() , 设置图片大小
• plt. plot(x, y), 画图
• plt.grid(), 绘制网格线
• plt.axis(), 设置坐标轴范围
• plt.xlim() , 设置X轴范围
• plt.ylim(), 设置Y轴范围

import numpy as np
import matplotlib.pyplot as plt
# 1、图形绘制
x = np.linspace(0,2*np.pi) # x轴
# y轴
y = np.sin(x) # 正弦
# 绘制线形图
# 调整尺寸
plt.figure(figsize=(9,6))
plt.plot(x,y)
# 继续调用plot绘制多条线形图
# 2、设置网格线
plt.grid(linestyle = '--',# 样式color = 'green',# 颜色alpha = 0.75) # 透明度
# 3、设置坐标轴范围
plt.axis([-1,10,-1.5,1.5])
plt.xlim([-1,10])
plt.ylim([-1.5,1.5])

第二节 坐标轴刻度、标签、标题

• plt.xticks(), 设置X轴刻度
• labels , 设置标签
• plt.rcParams(), 设置字体
• plt.title('正弦波')     # 设置标题

import numpy as np
import matplotlib.pyplot as plt
# 1、图形绘制
x = np.linspace(0,2*np.pi) # x轴
# y轴
y = np.sin(x) # 正弦
plt.plot(x,y)
# 2、设置x轴y轴刻度
plt.xticks(np.arange(0,7,np.pi/2))
plt.yticks([-1,0,1])
# 3、设置x轴y轴刻度标签
_ = plt.yticks(ticks = [-1,0,1],labels=['min','0','max'],fontsize=20, ha='right')
font={'family':'serif','style':'italic','weight':'normal','color':'red','size':16}
_ = plt.xticks(ticks = np.arange(0,7,np.pi/2),# LaTex语法，输入格式为：r'$\sigma$'  #其中的sigma对应于希腊字母的σlabels=['0',r'$\frac{\pi}{2}$',r'$\pi$',r'$\frac{3\pi}{2}$',r'$2\pi$'],fontsize = 20,fontweight = 'normal',color = 'red')
# 4、坐标轴标签，标题
plt.ylabel('y = sin(x)',rotation = 0,horizontalalignment = 'right',fontstyle = 'normal',fontsize = 20)
# 获取电脑上的字体库
from matplotlib.font_manager import FontManager
# fm = FontManager()
# mat_fonts = set(f.name for f in fm.ttflist)  # 遍历所有字体
# print(mat_fonts)  # 获取电脑自带字体列表
plt.rcParams['font.sans-serif'] = 'Adobe Heiti Std' # 设置宋体，显示中文
plt.title('正弦波')  # 设置标题

第三节 设置图例

• plt.legend(['Sin','Cos']),  设置图例

import numpy as np
import matplotlib.pyplot as plt# 1、图形绘制
x = np.linspace(0,2*np.pi) # x轴
# y轴
y = np.sin(x) # 正弦
# 绘制线形图
# 调整尺寸
plt.figure(figsize=(9,6))
plt.plot(x,y)# 2、图例
plt.plot(x,np.cos(x)) # 余弦波
plt.legend(['Sin','Cos'],fontsize = 18,loc = 'center',ncol = 2,bbox_to_anchor = [0,1.05,1,0.2])

第四节 脊柱移动 (坐标轴移动)

• ax.spines[ 'left' ].set_position(('data',0))  # 坐标轴调整: 'left, bottom, top, right'

import numpy as np
import matplotlib.pyplot as plt
x = np.linspace(-np.pi,np.pi,50)
plt.rcParams['axes.unicode_minus'] = False
plt.figure(figsize=(9,6))
plt.plot(x,np.sin(x),x,np.cos(x))
ax = plt.gca() # 获取当前视图
# 右边和上面脊柱消失zz
ax.spines['right'].set_color('white')
ax.spines['top'].set_color('#FFFFFF')# 设置下面左边脊柱位置，data表示数据，axes表示相对位置0~1
ax.spines['bottom'].set_position(('data',0))
ax.spines['left'].set_position(('data',0))plt.yticks([-1,0,1],labels=['-1','0','1'],fontsize = 18)
_ = plt.xticks([-np.pi,-np.pi/2,np.pi/2,np.pi],labels=[r'$-\pi$',r'$-\frac{\pi}{2}$',r'$\frac{\pi}{2}$',r'$\pi$'],fontsize = 18)

第五节 图片保存

• plt.savefig(), 保存图片

import numpy as np
import matplotlib.pyplot as plt
# 1、图形绘制
x = np.linspace(0,2*np.pi) # x轴
# y轴
y = np.sin(x) # 正弦波
plt.figure(linewidth = 4)
plt.plot(x,y,color = 'red')
plt.plot(x,np.cos(x),color = 'k') # 余弦波ax = plt.gca() # 获取视图
ax.set_facecolor('lightgreen') # 设置视图背景颜色
# 2、图例
plt.legend(['Sin','Cos'],fontsize = 18,loc = 'center',ncol = 2,bbox_to_anchor = [0,1.05,1,0.2])
# plt.tight_layout() # 自动调整布局空间，就不会出现图片保存不完整
plt.savefig('./基础5.png',    # 文件名：png、jpg、pdfdpi = 100,        # 保存图片像素密度facecolor = 'violet',  # 视图与边界之间颜色设置edgecolor = 'lightgreen',  # 视图边界颜色设置bbox_inches = 'tight')     # 保存图片完整

第二部分 风格和样式

第一节 颜色、线形、点形、线宽、透明度

import numpy as np
import matplotlib.pyplot as plt
x = np.linspace(0,2*np.pi,20)
y1 = np.sin(x)
y2 = np.cos(x)# 设置颜色，线型，点型
plt.plot(x,y1,color = 'indigo',ls = '-.',marker = 'p')
plt.plot(x,y2,color = '#FF00EE',ls = '--',marker = 'o')
plt.plot(x,y1 + y2,color = (0.2,0.7,0.2),marker = '*',ls = ':')
plt.plot(x,y1 + 2*y2,linewidth = 3,alpha = 0.7,color = 'orange') # 线宽、透明度
plt.plot(x,2*y1 - y2,'bo--') # 参数连用

第二节 更多属性设置

import numpy as np
import pandas as pd
def f(x):return np.exp(-x) * np.cos(2*np.pi*x)
x = np.linspace(0,5,50)
plt.figure(figsize=(9,6))
plt.plot(x,f(x),color = 'purple',marker = 'o',ls = '--',lw = 2,alpha = 0.6,markerfacecolor = 'red',   # 点颜色markersize = 10,# 点大小markeredgecolor = 'green', # 点边缘颜色markeredgewidth = 3)       # 点边缘宽度
plt.xticks(size = 18)               # 设置刻度大小
plt.yticks(size = 18)

第三部分 多图布局

第一节 子视图

• plt.subplot(2, 2, 2)      # 多图设置

import numpy as np
import matplotlib.pyplot as pltx = np.linspace(-np.pi,np.pi,50)
y = np.sin(x)# 子视图1
plt.figure(figsize=(9,6))
ax = plt.subplot(221) # 两行两列第一个子视图
ax.plot(x,y,color = 'red')
ax.set_facecolor('green') # 调用子视图设置方法，设置子视图整体属性
# 子视图2
ax = plt.subplot(2,2,2) # 两行两列第二个子视图
line, = ax.plot(x,-y) # 返回绘制对象
line.set_marker('*') # 调用对象设置方法，设置属性
line.set_markerfacecolor('red')
line.set_markeredgecolor('green')
line.set_markersize(10)
# 子视图3
ax = plt.subplot(2,1,2) # 两行一列第二行视图
plt.sca(ax) # 设置当前视图
x = np.linspace(-np.pi,np.pi,200)
plt.plot(x,np.sin(x*x),color = 'red')

 第二节 嵌套

import numpy as np
import matplotlib.pyplot as pltx = np.linspace(-np.pi,np.pi,25)
y = np.sin(x)
fig = plt.figure(figsize=(9,6)) # 创建视图plt.plot(x,y)# 嵌套方式一，axes轴域（横纵坐标范围），子视图
ax = plt.axes([0.2,0.55,0.3,0.3]) # 参数含义[left, bottom, width, height]
ax.plot(x,y,color = 'g')# 嵌套方式二
ax = fig.add_axes([0.55,0.2,0.3,0.3]) # 使用视图对象添加子视图
ax.plot(x,y,color = 'r')

第三节 多图布局分格显示

3.3.1 均匀布局

import numpy as np
import matplotlib.pyplot as plt
x = np.linspace(0,2*np.pi)
# sharex:所有小图共享x轴  sharey:表示所有小图共享y轴  坐标轴以所有小图中范围最大的进行显示
fig, ((ax11,ax12,ax13), (ax21,ax22,ax23),(ax31,ax32,ax33)) = plt.subplots(3, 3)
# 也可通过plt.subplot() 一个个添加子视图
fig.set_figwidth(9)
fig.set_figheight(6)
ax11.plot(x,np.sin(x))
ax12.plot(x,np.cos(x))
ax13.plot(x,np.tanh(x))
ax21.plot(x,np.tan(x))
ax22.plot(x,np.cosh(x))
ax23.plot(x,np.sinh(x))
ax31.plot(x,np.sin(x) + np.cos(x))
ax32.plot(x,np.sin(x*x) + np.cos(x*x))
ax33.plot(x,np.sin(x)*np.cos(x))
# 紧凑显示，边框会比较小，可以注释掉该行查看效果
plt.tight_layout()
plt.show()

 3.3.2 不均匀分布

 方式一:
plt.subplot(3, 3, (4, 5)), 将4,5图 画到一块

import numpy as np
import matplotlib.pyplot as plt
# 需要导入gridspec模块
x = np.linspace(0,2*np.pi,200)
fig = plt.figure(figsize=(12,9))
# 使用切片方式设置子视图
ax1 = plt.subplot(3,1,1) # 视图对象添加子视图
ax1.plot(x,np.sin(10*x))
# 设置ax1的标题，xlim、ylim、xlabel、ylabel等所有属性现在只能通过set_属性名的方法设置
ax1.set_title('ax1_title')  # 设置小图的标题
ax2 = plt.subplot(3,3,(4,5))
ax2.set_facecolor('green')
ax2.plot(x,np.cos(x),color = 'red')
ax3 = plt.subplot(3,3,(6,9))
ax3.plot(x,np.sin(x) + np.cos(x))
ax4 = plt.subplot(3,3,7)
ax4.plot([1,3],[2,4])
ax5 = plt.subplot(3,3,8)
ax5.scatter([1,2,3], [0,2, 4])
ax5.set_xlabel('ax5_x',fontsize = 12)
ax5.set_ylabel('ax5_y',fontsize = 12)
plt.show()

方式二:

• plt.subplot2grid((3, 3), (1, 0), colspan = 2)

import numpy as np
import matplotlib.pyplot as plt
x = np.linspace(0,2*np.pi,100)
plt.figure(figsize=(12,9))
# 子视图1
ax1 = plt.subplot2grid(shape = (3, 3),# 布局形状loc =  (0, 0), # 布局绘制位置colspan=3) # 跨几列
ax1.plot(x,np.sin(10*x))
# 设置ax1的标题，xlim、ylim、xlabel、ylabel等所有属性现在只能通过set_属性名的方法设置
ax1.set_title('ax1_title')  # 设置小图的标题
# 子视图2
ax2 = plt.subplot2grid((3, 3), (1, 0), colspan=2) # 跨两列
ax2.set_facecolor('green')
ax2.plot(x,np.cos(x),color = 'red')
# 子视图3
ax3 = plt.subplot2grid((3, 3), (1, 2), rowspan=2) # 跨两行
ax3.plot(x,np.sin(x) + np.cos(x))
# 子视图4
ax4 = plt.subplot2grid((3, 3), (2, 0))
ax4.plot([1,3],[2,4])
# 子视图5
ax5 = plt.subplot2grid((3, 3), (2, 1))
ax5.scatter([1,2,3], [0,2, 4])
ax5.set_xlabel('ax5_x',fontsize = 12)
ax5.set_ylabel('ax5_y',fontsize = 12)

方式三

• gs = gridspec.GridSpec(3, 3)
• plt.subplot(gs[1:, :2])

import numpy as np
import matplotlib.pyplot as plt
# 需要导入gridspec模块
import matplotlib.gridspec as gridspec
x = np.linspace(0,2*np.pi,200)
fig = plt.figure(figsize=(12,9))
# 将整个视图分成3x3布局
gs = gridspec.GridSpec(3, 3)
# 使用切片方式设置子视图
ax1 = fig.add_subplot(gs[0,:]) # 视图对象添加子视图
ax1.plot(x,np.sin(10*x))
# 设置ax1的标题，xlim、ylim、xlabel、ylabel等所有属性现在只能通过set_属性名的方法设置
ax1.set_title('ax1_title')  # 设置小图的标题
ax2 = plt.subplot(gs[1, :2]) # 模块调用
ax2.set_facecolor('green')
ax2.plot(x,np.cos(x),color = 'red')
# 从第一行到最后，占1、2两行，后面的2表示只占用第二列，也就是最后的一列
ax3 = plt.subplot(gs[1:, 2])
ax3.plot(x,np.sin(x) + np.cos(x))
# 倒数第一行，只占第0列这一列
ax4 = plt.subplot(gs[-1, 0])
ax4.plot([1,3],[2,4])
# 倒数第一行，只占倒数第二列，由于总共三列，所以倒数第二列就是序号1的列
ax5 = plt.subplot(gs[-1, -2])
ax5.scatter([1,2,3], [0,2, 4])
ax5.set_xlabel('ax5_x',fontsize = 12)
ax5.set_ylabel('ax5_y',fontsize = 12)
plt.show()

第四节 双轴显示

• plt.rcParams , 字体设置
• ax1.twinx(), 创建镜像坐标

import numpy as np
import matplotlib.pyplot as pltx = np.linspace(-np.pi,np.pi,100)
data1 = np.exp(x)
data2 = np.sin(x)plt.figure(figsize=(9,6))
plt.rcParams['font.size'] = 16 # 设置整体字体大小ax1 = plt.gca() # 获取当前轴域ax1.set_xlabel('time (s)') # 设置x轴标签
ax1.set_ylabel('exp', color='red') # 设置y轴标签
ax1.plot(x, data1, color='red') # 数据绘制
ax1.tick_params(axis='y', labelcolor='red') # 设置y轴刻度属性ax2 = ax1.twinx()  # 创建新axes实例，共享x轴，并设置
ax2.set_ylabel('sin', color='blue')
ax2.plot(x, data2, color='blue')
ax2.tick_params(axis='y', labelcolor='blue')plt.tight_layout() # 紧凑布局

第四部分 文本、注释、箭头

第一节 文本

import numpy as np
import matplotlib.pyplot as plt# 字体属性
font = {'fontsize': 20,'family': 'Adobe Heiti Std','color':  'red','weight': 'bold'}x = np.linspace(0.0, 5.0, 100)
y = np.cos(2*np.pi*x) * np.exp(-x)plt.figure(figsize=(9,6))
plt.plot(x, y, 'k')
plt.title('exponential decay',fontdict=font)
plt.suptitle('指数衰减',y = 1.05,fontdict = font,fontsize = 30)
plt.text(x = 2, y = 0.65, # 横纵坐标位置s = r'$\cos(2 \pi t) \exp(-t)$') # 文本内容
plt.xlabel('time (s)')
plt.ylabel('voltage (mV)')
plt.show()

第二节 箭头

import matplotlib.pyplot as plt
import numpyloc = np.random.randint(0,10,size = (10,2))
plt.figure(figsize=(10, 10))
plt.plot(loc[:, 0], loc[:, 1], 'g*', ms=20)   # 画绿色的星星
plt.grid(True)   # 框架线是否显示# 路径
way = np.arange(10)
np.random.shuffle(way)for i in range(0, len(way)-1):start = loc[way[i]]end = loc[way[i+1]]plt.arrow(start[0], start[1], end[0]-start[0], end[1]-start[1], # 坐标与距离head_width=0.2, lw=2,#箭头长度，箭尾线宽length_includes_head = True) # 长度计算包含箭头箭尾plt.text(start[0],start[1],s = i,fontsize = 18,color = 'red') # 文本if i == len(way) - 2: # 最后一个点plt.text(end[0],end[1],s = i + 1,fontsize = 18,color = 'red')

第三节 注释

import numpy as np
import matplotlib.pyplot as plt
fig, ax = plt.subplots()
x = np.arange(0.0, 5.0, 0.01)
y = np.cos(2*np.pi*x)
line, = ax.plot(x,y,lw=2)
ax.annotate('local max', # 文本内容xy=(2, 1),  # 箭头指向位置xytext=(3, 1.5), # 文本位置arrowprops=dict(facecolor='black', shrink=0.05)) # 箭头
ax.annotate('local min',xy = (2.5,-1),xytext = (4,-1.8),arrowprops = dict(facecolor = 'black',width = 2, # 箭头宽度headwidth = 10,# 箭头头部宽度headlength = 10, # 箭头头部长度shrink = 0.1)) # 箭头两端收缩的百分比（占总长）
ax.annotate('median',xy = (2.25,0),xytext = (0.5,-1.8),arrowprops = dict(arrowstyle = '-|>'), # 箭头样式fontsize = 20)
ax.set_ylim(-2, 2)

第四节 注释箭头连接形状

• ax.annotate()       # 注释

import matplotlib.pyplot as pltdef annotate_con_style(ax, connectionstyle):x1, y1 = 3,2x2, y2 = 8,6ax.plot([x1, x2], [y1, y2], ".")ax.annotate('',xy=(x1, y1), # 相当于B点，arrow headxytext=(x2, y2), # 相当于A点，arrow tailarrowprops=dict(arrowstyle='->', color='red',shrinkA = 5,shrinkB = 5,connectionstyle=connectionstyle))ax.text(0.05, 0.95, connectionstyle.replace(",", "\n"),transform=ax.transAxes, # 相对坐标fontsize=9, ha="left", va="top")# 指定对齐方式# 常用箭头连接样式
fig, axs = plt.subplots(3, 5, figsize=(9,6))
# display(axs.shape,axs[0, 0])
annotate_con_style(axs[0, 0], "angle3,angleA=90,angleB=0")
annotate_con_style(axs[1, 0], "angle3,angleA=0,angleB=90")
annotate_con_style(axs[2, 0], "angle3,angleA = 0,angleB=150")
annotate_con_style(axs[0, 1], "arc3,rad=0.")
annotate_con_style(axs[1, 1], "arc3,rad=0.3")
annotate_con_style(axs[2, 1], "arc3,rad=-0.3")
annotate_con_style(axs[0, 2], "angle,angleA=-90,angleB=180,rad=0")
annotate_con_style(axs[1, 2], "angle,angleA=-90,angleB=180,rad=5")
annotate_con_style(axs[2, 2], "angle,angleA=-90,angleB=10,rad=5")
annotate_con_style(axs[0, 3], "arc,angleA=-90,angleB=0,armA=30,armB=30,rad=0")
annotate_con_style(axs[1, 3], "arc,angleA=-90,angleB=0,armA=30,armB=30,rad=5")
annotate_con_style(axs[2, 3], "arc,angleA=-90,angleB=0,armA=0,armB=40,rad=0")
annotate_con_style(axs[0, 4], "bar,fraction=0.3")
annotate_con_style(axs[1, 4], "bar,fraction=-0.3")
annotate_con_style(axs[2, 4], "bar,angle=180,fraction=-0.2")for ax in axs.flat:# 设置轴域刻度ax.set(xlim=(0, 10), ylim=(0, 10),xticks = [],yticks = [],aspect=1)
fig.tight_layout(pad=0.2)

第五部分 常用视图

第一节 折线图

• plt.plot(x, marker = '*', color = 'r')      # 直接画

import numpy as np
import matplotlib.pyplot as plt
x = np.random.randint(0,10,size = 15)
# 一图多线
plt.figure(figsize=(9,6))
plt.plot(x,marker = '*',color = 'r')   # 折线图
plt.plot(x.cumsum(),marker = 'o')# 多图布局
fig,axs = plt.subplots(2,1)
fig.set_figwidth(9)
fig.set_figheight(6)
axs[0].plot(x,marker = '*',color = 'red')
axs[1].plot(x.cumsum(),marker = 'o')

第二节 柱状图

堆叠柱状图

• plt.bar()

import numpy as np
import matplotlib.pyplot as plt
labels = ['G1', 'G2', 'G3', 'G4', 'G5','G6'] # 级别
men_means = np.random.randint(20,35,size = 6)
women_means = np.random.randint(20,35,size = 6)
men_std = np.random.randint(1,7,size = 6)
women_std = np.random.randint(1,7,size = 6)
width = 0.35
plt.bar(labels, # 横坐标men_means, # 柱高width, # 线宽yerr=4, # 误差条label='Men')#标签
plt.bar(labels, women_means, width, yerr=2, bottom=men_means,label='Women')
plt.ylabel('Scores', fontsize = 12)
plt.title('Scores by group and gender', fontsize = 12)
plt.legend(fontsize = 12)

分组柱状图

• plt.bar()

import matplotlib
import matplotlib.pyplot as plt
import numpy as nplabels = ['G1', 'G2', 'G3', 'G4', 'G5','G6'] # 级别
men_means = np.random.randint(20,35,size = 6)
women_means = np.random.randint(20,35,size = 6)
x = np.arange(len(men_means))
plt.figure(figsize=(9,6))
rects1 = plt.bar(x - width/2, men_means, width) # 返回绘图区域对象
rects2 = plt.bar(x + width/2, women_means, width)# 设置标签标题，图例
plt.ylabel('Scores', fontsize = 12)
plt.title('Scores by group and gender', fontsize = 12)
plt.xticks(x,labels, fontsize = 9)
plt.legend(['Men','Women'], fontsize = 12)# 添加注释
def set_label(rects):for rect in rects:height = rect.get_height() # 获取高度plt.text(x = rect.get_x() + rect.get_width()/2, # 水平坐标y = height + 0.5, # 竖直坐标s = height, # 文本fontsize = 12, ha = 'center') # 水平居中set_label(rects1)
set_label(rects2)
plt.tight_layout() # 设置紧凑布局
plt.savefig('./分组带标签柱状图.png')

第三节 极坐标图

极坐标线形图:

• plt.subplot()

import numpy as np
import matplotlib.pyplot as pltr = np.arange(0, 4*np.pi, 0.01) # 弧度值
y = np.linspace(0,2,len(r)) # 目标值ax = plt.subplot(111,projection = 'polar',facecolor = 'lightgreen') # 定义极坐标
ax.plot(r, y,color = 'red')
ax.set_rmax(3) # 设置半径最大值
ax.set_rticks([0.5, 1, 1.5, 2])  # 设置半径刻度
ax.set_rlabel_position(-22.5)  # 设置半径刻度位置
ax.grid(True) # 网格线ax.set_title("A line plot on a polar axis", va='center',ha = 'center',pad = 30)

极坐标柱状图:

• plt.subplot(, projection = ‘polar’) , 圆图,圈圈

import numpy as np
import matplotlib.pyplot as plt
N = 8 # 分成8份
theta = np.linspace(0.0, 2 * np.pi, N, endpoint=False)
radii = np.random.randint(3,15,size = N)
width = np.pi / 4
colors = np.random.rand(8,3) # 随机生成颜色
ax = plt.subplot(111, projection='polar') # polar表示极坐标
ax.bar(theta, radii, width=width, bottom=0.0,color = colors)

 第四节 直方图

• ax.hist() , 直方图

import numpy as np
import matplotlib.pyplot as pltmu = 100 # 平均值
sigma = 15 # 标准差
x = np.random.normal(loc = mu,scale = 15,size = 10000)
fig, ax = plt.subplots()n, bins, patches = ax.hist(x, 200, density=True) # 直方图# 概率密度函数
y = ((1 / (np.sqrt(2 * np.pi) * sigma)) *np.exp(-0.5 * (1 / sigma * (bins - mu))**2))
plt.plot(bins, y, '--')
plt.xlabel('Smarts', fontsize=12)
plt.ylabel('Probability density', fontsize=12)
plt.title(r'Histogram of IQ: $\mu=100$, $\sigma=15$', fontsize=12)# 紧凑布局
fig.tight_layout()
plt.savefig('./直方图.png')

第五节 箱形图

• plt.boxplot() ,箱形图
• 用来找异常值

import numpy as np
import matplotlib.pyplot as plt
data=np.random.normal(size=(500,4)) 
lables = ['A','B','C','D']
# 用Matplotlib画箱线图
plt.boxplot(data,1,'gD',labels=lables) # 红色的圆点是异常值

 第六节 散点图

• 散点图的英文叫做 scatter plot，它将两个变量的值显示在二维坐标中，非常适合展示两个变量之间的关系.
• plt.scatter()

import numpy as np
import matplotlib.pyplot as plt
data = np.random.randn(100,2)
s = np.random.randint(100,300,size = 100)
color = np.random.randn(100)
plt.scatter(data[:,0], # 横坐标data[:,1], # 纵坐标s = s, # 尺寸c = color, # 颜色alpha = 0.5) # 透明度

 第七节 饼图

一般饼图

• plt.pie()

import numpy as np
import matplotlib.pyplot as plt
# 解决中文字体乱码的问题
matplotlib.rcParams['font.sans-serif']='Adobe Heiti Std' labels =["五星","四星","三星","二星","一星"] # 标签
percent = [95,261,105,30,9] # 某市星级酒店数量# 设置图片大小和分辨率
fig=plt.figure(figsize=(5,5), dpi=150)
# 偏移中心量，突出某一部分
explode = (0, 0.1, 0, 0, 0) 
# 绘制饼图：autopct显示百分比，这里保留一位小数；shadow控制是否显示阴影
plt.pie(x = percent, # 数据explode=explode, # 偏移中心量labels=labels,  # 显示标签autopct='%0.1f%%', # 显示百分比shadow=True) # 阴影，3D效果
plt.savefig("./饼图.jpg")

 嵌套饼图

• plt.pie()
• 根据圆的半径进行拆分内外圆

import pandas as pd
import matplotlib.pyplot as plt
food = pd.read_excel('./food.xlsx')
# 分组聚合，内圈数据
inner = food.groupby(by = 'type')['花费'].sum()
outer = food['花费'] # 外圈数据
plt.rcParams['font.family'] = 'Kaiti SC'
plt.rcParams['font.size'] = 18
fig=plt.figure(figsize=(8,8))
# 绘制内部饼图
plt.pie(x = inner, # 数据radius=0.6, # 饼图半径wedgeprops=dict(linewidth=3,width=0.6,edgecolor='w'),# 饼图格式：间隔线宽、饼图宽度、边界颜色labels = inner.index, # 显示标签labeldistance=0.4) # 标签位置
# 绘制外部饼图
plt.pie(x = outer, radius=1, # 半径wedgeprops=dict(linewidth=3,width=0.3,edgecolor='k'),# 饼图格式：间隔线宽、饼图宽度、边界颜色labels = food['食材'], # 显示标签labeldistance=1.2) # 标签位置# 设置图例标题，bbox_to_anchor = (x, y, width, height)控制图例显示位置
plt.legend(inner.index,bbox_to_anchor = (0.9,0.6,0.4,0.4),title = '食物占比')
plt.tight_layout()
plt.savefig('./嵌套饼图.png',dpi = 200)

甜甜圈

• plt.pie()

import numpy as np
import matplotlib.pyplot as pltplt.figure(figsize=(6,6))
# 甜甜圈原料
recipe = ["225g flour","90g sugar","1 egg","60g butter","100ml milk","1/2package of yeast"]
# 原料比例
data = [225, 90, 50, 60, 100, 5]
wedges, texts = plt.pie(data,startangle=40)
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")for i, p in enumerate(wedges):ang = (p.theta2 - p.theta1)/2. + p.theta1 # 角度计算# 角度转弧度----->弧度转坐标y = np.sin(np.deg2rad(ang))x = np.cos(np.deg2rad(ang))ha = {-1: "right", 1: "left"}[int(np.sign(x))] # 水平对齐方式connectionstyle = "angle,angleA=0,angleB={}".format(ang) # 箭头连接样式kw["arrowprops"].update({"connectionstyle": connectionstyle}) # 更新箭头连接方式plt.annotate(recipe[i], xy=(x, y), xytext=(1.35*np.sign(x), 1.4*y),ha=ha,**kw,fontsize = 12,weight = 'bold')
plt.title("Matplotlib bakery: A donut",fontsize = 12,pad = 25)
plt.tight_layout()

 第八节 热力图

• plt.imshow(harvest)

import numpy as np
import matplotlib
import matplotlib.pyplot as pltvegetables = ["cucumber", "tomato", "lettuce", "asparagus","potato", "wheat", "barley"]
farmers = list('ABCDEFG')harvest = np.random.rand(7,7)*5 # 农民丰收数据plt.rcParams['font.size'] = 18
plt.rcParams['font.weight'] = 'heavy'
plt.figure(figsize=(9,9))
im = plt.imshow(harvest)plt.xticks(np.arange(len(farmers)),farmers,rotation = 45,ha = 'right', fontsize=12)
plt.yticks(np.arange(len(vegetables)),vegetables, fontsize=12)# 绘制文本
for i in range(len(vegetables)):for j in range(len(farmers)):text = plt.text(j, i, round(harvest[i, j],1),ha="center", va="center", color='r', fontsize=12)
plt.title("Harvest of local farmers (in tons/year)",pad = 20, fontsize=12)
fig.tight_layout()
plt.savefig('./热力图.png')

 第九节 面积图

• plt.stackplot()

import matplotlib.pyplot as plt
plt.figure(figsize=(9,6))
days = [1,2,3,4,5]  
sleeping =[7,8,6,11,7]
eating = [2,3,4,3,2]
working =[7,8,7,2,2]
playing = [8,5,7,8,13]   
plt.stackplot(days,sleeping,eating,working,playing)  
plt.xlabel('x')
plt.ylabel('y')
plt.title('Stack Plot',fontsize = 12)
plt.legend(['Sleeping','Eating','Working','Playing'],fontsize = 12)

 第十节 蜘蛛图

• fig.add_subplot(, polar = True), 画圈极地图.

import numpy as np
import matplotlib.pyplot as plt
plt.rcParams['font.family'] = 'Adobe Heiti Std'
labels=np.array(["个人能力","IQ","服务意识","团队精神","解决问题能力","持续学习"])
stats=[83, 61, 95, 67, 76, 88]
# 画图数据准备，角度、状态值
angles=np.linspace(0, 2*np.pi, len(labels), endpoint=False)
stats=np.concatenate((stats,[stats[0]]))
angles=np.concatenate((angles,[angles[0]]))
# 用Matplotlib画蜘蛛图
fig = plt.figure(figsize=(9,9))
ax = fig.add_subplot(111, polar=True)   
ax.plot(angles, stats, 'o-', linewidth=2) # 连线
ax.fill(angles, stats, alpha=0.25) # 填充
# 设置角度
ax.set_thetagrids(angles*180/np.pi,#角度值labels,fontsize = 18)
ax.set_rgrids([20,40,60,80],fontsize = 18)

第六部分 3D图形

第一节 三维折线图散点图

• fig = plt.figure() , 二维图像, 定义图大小
• ax3 = Axes3D(fig) , 二维转三维
• ax3.plot()     # 画图

import numpy as np
import matplotlib.pyplot as plt
from mpl_toolkits.mplot3d.axes3d import Axes3D # 3D引擎x = np.linspace(0,60,300)
y = np.sin(x)
z = np.cos(x)fig = plt.figure(figsize=(9,6)) # 二维图形
ax3 = Axes3D(fig) # 二维变成了三维
ax3.plot(x,y,z) # 3维折线图
# 3维散点图
ax3.scatter(np.random.rand(50)*60,np.random.rand(50),np.random.rand(50),color = 'red',s = 100)

第二节 三维柱状图

• fig = plt.figure() , 二维图像, 定义图大小
• ax3 = Axes3D(fig) , 二维转三维
• ax3.bar()

import numpy as np
import matplotlib.pyplot as plt
from mpl_toolkits.mplot3d.axes3d import Axes3D # 3D引擎
month = np.arange(1,5)
# 每个月 4周 每周都会产生数据
# 三个维度：月、周、销量
fig = plt.figure(figsize=(9,6))
ax3 = Axes3D(fig)for m in month:ax3.bar(np.arange(4),np.random.randint(1,10,size = 4),zs = m ,zdir = 'x',# 在哪个方向上，一排排排列alpha = 0.7,# alpha 透明度width = 0.5)
ax3.set_xlabel('X',fontsize = 18,color = 'red')
ax3.set_ylabel('Y',fontsize = 18,color = 'red')
ax3.set_zlabel('Z',fontsize = 18,color = 'green')