Matplotlib数据可视化（3）Python数据分析

1 Matplotlib简介

• 自从numpy和pandas数据分析的广泛应用，人们发现虽然可以对大量的数据进行快速方便的，各种各样的操作，但是对结果查看不够直观，因此，Matplotlib应运而生，在数据分析与机器学习中，我们经常要用到大量的可视化操作。一张制作精美的数据图片，可以展示大量的信息，一图顶千言。而在可视化中，Matplotlib算得上是最常用的工具。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。
• numpy，pandas，Matplotlib被成为数据分析不可或缺的的三剑客。
• 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。
  
• 安装方式：
  安装了python之后，使用bash命令即可（建议使用anaconda，方便不同python环境的切换）：

pip install matplotlib -i https://pypi.tuna.tsinghua.edu.cn/simple

2 基础知识 2.1 图形绘制

import numpy as np
import matplotlib.pyplot as plt
# 1、图形绘制
x = np.linspace(0,2*np.pi) # x轴  默认生成50个数
y = np.sin(x) # y轴 正弦
# 绘制线形图 
plt.figure(figsize=(9,6)) # 调整尺寸 
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]) # 显示的范围是[-x,x,-y,y]
plt.xlim([-1,10])
plt.ylim([-1.5,1.5])

2.2 坐标轴刻度、标签、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  默认生成50个数
y = np.sin(x) # y轴 正弦
plt.plot(x,y)
# 2、设置x轴y轴刻度 
plt.xticks(np.arange(0,7,np.pi/2)) # 0到7中作二分之派的等差数列
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') # _一般表示会返回数据但是不需要后续操作时
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σ 
               labels = ['0',r'$frac{pi}{2}$',r'$pi$',r'$frac{3pi} {2}$',r'$2pi$'],
               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) 
# print(mat_fonts) 
plt.rcParams['font.sans-serif'] = 'STZhongsong' 
# 设置宋体，显示中文 
plt.title('正弦波')

2.3 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 = np.sin(x) # y轴 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])

2.4 脊柱移动

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 
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)

2.5 图片保存

import numpy as np 
import matplotlib.pyplot as plt 
# 1、图形绘制 
x = np.linspace(0,2*np.pi) # x轴
y = np.sin(x) # y轴  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、pdf 
            dpi = 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

3 风格和样式 3.1 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(b代表蓝色，o代表marker是大圆点)

3.2 更多属性设置

import numpy as np
import matplotlib.pyplot as plt

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XY轴刻度大小
_ = plt.yticks(size = 18)

4 多图布局 4.1 子视图

import numpy as np 
import matplotlib.pyplot as plt 
x = 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')

4.2 嵌套

import numpy as np 
import matplotlib.pyplot as plt 
x = 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')

4.3 多图布局分格显示 4.3.1 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()

4.3.2 不均匀布局

• 方式一

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 （一共生成三成3行一列份，把第一行占了）
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 
    # 一共生成三成3行3列9份，按照由左向右，从上到下的顺序
ax2 = plt.subplot(3,3,(4,5)) # 分成了9份，占第4个第5个位置
ax2.set_facecolor('green') 
ax2.plot(x,np.cos(x),color = 'red') 
ax3 = plt.subplot(3,3,(6,9)) # 分成了9份，占第6个和第9个位置
# 如果乱占用其他的份数，会覆盖原本画好的图像
# ax3 = plt.subplot(3,3,(6,7))
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

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 (从0开始)
                       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)) # 默认只占一个位置
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

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 （占第一行的所有，即（0,0）(0,1)(0,2)份）
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()

4.4 双轴显示

import numpy as np 
import matplotlib.pyplot as plt 

x = 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(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(data2, color='blue') 
ax2.tick_params(axis='y', labelcolor='blue') 
plt.tight_layout() # 紧凑布局

5 文本、注释、箭头

• 常用函数如下：

5.1 文本

import numpy as np 
import matplotlib.pyplot as plt 


# 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) 
# print(mat_fonts)
# 字体属性 
font = {'fontsize': 20, 'family': 'STCaiyun', '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()

5.2 箭头

import matplotlib.pyplot as plt 
import numpy as np

loc = np.random.randint(0,10,size = (10,2)) 
plt.figure(figsize=(10, 10)) 
plt.plot(loc[:,0], loc[:,1], 'g*', ms=20) 
plt.grid(True) # 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')

5.3 注释

import numpy as np
import pandas as pd
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) # lw: line weight 线的粗细度
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)

5.4 注释箭头连接形状

import matplotlib.pyplot as plt 
def annotate_con_style(ax, connectionstyle): 
    x1, y1 = 3,2 
    x2, y2 = 8,6 
    ax.plot([x1, x2], [y1, y2], ".") 
    ax.annotate(" ", 
                xy=(x1, y1), # 相当于B点，arrow head 
                xytext=(x2, y2), # 相当于A点，arrow tail 
                arrowprops=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 
            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))
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)

6 常用视图 6.1 折线图

import numpy as np 
import matplotlib.pyplot as plt 
x = np.random.randint(0,10,size = 15) 
print(x)
# 一图多线 
plt.figure(figsize=(9,6)) 
plt.plot(x,marker = '*',color = 'r') # x默认是第几个数，y默认为x的大小
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')

6.2 柱状图

• 堆叠柱状图

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

• 分组带标签柱状图

import matplotlib 
import matplotlib.pyplot as plt 
import numpy as np 

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) 
x = np.arange(len(men_means))
width = 0.35 
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') 
plt.title('Scores by group and gender') 
plt.xticks(x,labels) 
plt.legend(['Men','Women']) 

# 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 
                 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')

6.3 极坐标图

• 极坐标线形图

import numpy as np 
import matplotlib.pyplot as plt 

r = 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

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)

6.4 直方图

import numpy as np
import matplotlib.pyplot as plt

mu = 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')
plt.ylabel('Probability density')
plt.title(r'Histogram of IQ: $mu=100$, $sigma=15$')

# 紧凑布局
fig.tight_layout()
plt.savefig('./直方图.png')

6.5 箱型图

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

6.6 散点图

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

6.7 饼图 6.7.1 一般饼图

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']='Kaiti SC' 

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")

6.7.2 嵌套饼图

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)

6.7.3 甜甜圈

import numpy as np
import matplotlib.pyplot as plt

plt.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 = 18,weight = 'bold')
plt.title("Matplotlib bakery: A donut",fontsize = 18,pad = 25)
plt.tight_layout()

6.8 热力图

import numpy as np
import matplotlib
import matplotlib.pyplot as plt

vegetables = ["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')
plt.yticks(np.arange(len(vegetables)),vegetables)

# 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')
plt.title("Harvest of local farmers (in tons/year)",pad = 20)
fig.tight_layout()
plt.savefig('./热力图.png')

6.9 面积图

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 = 18)
plt.legend(['Sleeping','Eating','Working','Playing'],fontsize = 18)

6.10 蜘蛛图

import numpy as np
import matplotlib.pyplot as plt
plt.rcParams['font.family'] = 'Kaiti SC'
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)

7 3D绘图 7.1 三维折线图散点图

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)

7.2 三维柱状图

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')