• 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σ
               labels=['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、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

第二部分 风格和样式

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

• plt.subplot(2, 2, 2)      # 多图设置

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')

 第二节 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')

第三节 多图布局分格显示

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 numpy

loc = 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 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
            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

• 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 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))
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 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:

• 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 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', 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

• 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')