1 概述

import matplotlib.pyplot as plt
import numpy as np

#step1
#用matplotlib.pyplot.figure()创建一个Figure个例
fig = plt.figure()

#step 2
#Figure实例创建一个两行一列的绘图区，并同时在第一个位置创建了一个subplot
ax = fig.add_subplot(2,1,1)#2rows,one column,first plot

#step 3
#用Axes实例方法画一条线
t = np.arange(0.0,1.0,0.01)
s = np.sin(2*np.pi*t)
line, = ax.plot(t,s,color='blue',lw=2)

2 基本元素

#设置Line2D的属性
x = range(0,5)
y = [2,5,7,8,10]

#1 直接在plot()函数中设置
plt.plot(x,y,linewidth=10);#设置线的粗细参数为10
#通过获得线对象，对线对象进行设置
line,=plt.plot(x,y,'-')
line.set_antialiased(False) # 关闭抗锯齿功能
#获得线属性，使用setp()函数设置
lines = plt.plot(x, y)
plt.setp(lines,color='r',linewidth=10)

#绘制line的方法
## 1. pyplot方法绘制
import matplotlib.pyplot as plt
x = range(0,5)
y = [2,5,7,8,10]
plt.plot(x,y);
# 2. Line2D对象绘制
import matplotlib.pyplot as plt
from matplotlib.lines import Line2D      

fig = plt.figure()
ax = fig.add_subplot(111)
line = Line2D(x, y)
ax.add_line(line)
ax.set_xlim(min(x), max(x))
ax.set_ylim(min(y), max(y))

plt.show()

import numpy as np
import matplotlib.pyplot as plt
fig = plt.figure()
x = np.arange(10)
y = 2.5 * np.sin(x / 20 * np.pi)
yerr = np.linspace(0.05, 0.2, 10)
plt.errorbar(x, y + 3, yerr=yerr, label='both limits (default)');

#hist绘制直方图
import matplotlib.pyplot as plt
import numpy as np 
x=np.random.randint(0,100,100) #生成[0-100)之间的100个数据,即 数据集 
bins=np.arange(0,101,10) #设置连续的边界值，即直方图的分布区间[0,10),[10,20)... 
plt.hist(x,bins,color='fuchsia',alpha=0.5)#alpha设置透明度，0为完全透明 
plt.xlabel('scores') 
plt.ylabel('count') 
plt.xlim(0,100)#设置x轴分布范围 
plt.show()

#Rectangle矩形类绘制直方图
import pandas as pd
import re
df = pd.DataFrame(columns = ['data'])
df.loc[:,'data'] = x
df['fenzu'] = pd.cut(df['data'], bins=bins, right = False,include_lowest=True)

df_cnt = df['fenzu'].value_counts().reset_index()
df_cnt.loc[:,'mini'] = df_cnt['index'].astype(str).map(lambda x:re.findall('\[(.*)\,',x)[0]).astype(int)
df_cnt.loc[:,'maxi'] = df_cnt['index'].astype(str).map(lambda x:re.findall('\,(.*)\)',x)[0]).astype(int)
df_cnt.loc[:,'width'] = df_cnt['maxi']- df_cnt['mini']
df_cnt.sort_values('mini',ascending = True,inplace = True)
df_cnt.reset_index(inplace = True,drop = True)

#用Rectangle把hist绘制出来
import matplotlib.pyplot as plt

fig = plt.figure()
ax1 = fig.add_subplot(111)

for i in df_cnt.index:
    rect =  plt.Rectangle((df_cnt.loc[i,'mini'],0),df_cnt.loc[i,'width'],df_cnt.loc[i,'fenzu'])
    ax1.add_patch(rect)

ax1.set_xlim(0, 100)
ax1.set_ylim(0, 16)
plt.show()

3 对象容器