import matplotlib.pyplot as plt
labels=‘frogs‘,‘hogs‘,‘dogs‘,‘logs‘
sizes=15,20,45,10
colors=‘yellowgreen‘,‘gold‘,‘lightskyblue‘,‘lightcoral‘
explode=0,0.1,0,0
plt.pie(sizes,explode=explode,labels=labels,colors=colors,autopct=‘%1.1f%%‘,shadow=True,startangle=50)
plt.axis(‘equal‘)
plt.show()

import matplotlib.pyplot as plt
plt.rcParams[‘font.sas-serig‘]=[‘SimHei‘] #用来正常显示中文标签
plt.rcParams[‘axes.unicode_minus‘]=False #用来正常显示负号

%matplotlib inline  #notebook模式下
%pylab inline   #ipython模式下

import numpy as np
import matplotlib.pyplot as plt

# evenly sampled time at 200ms intervals
t = np.arange(0., 5., 0.2)

# red dashes, blue squares and green triangles
plt.plot(t, t, ‘r--‘, t, t**2, ‘bs‘, t, t**3, ‘g^‘)
plt.show()

import matplotlib.pyplot as plt
plt.figure(1)                # 第一张图
plt.subplot(211)             # 第一张图中的第一张子图
plt.plot([1,2,3])
plt.subplot(212)             # 第一张图中的第二张子图
plt.plot([4,5,6])


plt.figure(2)                # 第二张图
plt.plot([4,5,6])            # 默认创建子图subplot(111)

plt.figure(1)                # 切换到figure 1 ; 子图subplot(212)仍旧是当前图
plt.subplot(211)             # 令子图subplot(211)成为figure1的当前图
plt.title(‘Easy as 1,2,3‘)   # 添加subplot 211 的标题

import numpy as np
import matplotlib.pyplot as plt

mu, sigma = 100, 15
x = mu + sigma * np.random.randn(10000)

# 数据的直方图
n, bins, patches = plt.hist(x, 50, normed=1, facecolor=‘g‘, alpha=0.75)


plt.xlabel(‘Smarts‘)
plt.ylabel(‘Probability‘)
#添加标题
plt.title(‘Histogram of IQ‘)
#添加文字
plt.text(60, .025, r‘$\mu=100,\ \sigma=15$‘)
plt.axis([40, 160, 0, 0.03])
plt.grid(True)
plt.show()

import numpy as np
import matplotlib.pyplot as plt

ax = plt.subplot(111)

t = np.arange(0.0, 5.0, 0.01)
s = np.cos(2*np.pi*t)
line, = plt.plot(t, s, lw=2)

plt.annotate(‘local max‘, xy=(2, 1), xytext=(3, 1.5),
            arrowprops=dict(facecolor=‘black‘, shrink=0.05),
            )

plt.ylim(-2,2)
plt.show()

# 导入 matplotlib 的所有内容（nympy 可以用 np 这个名字来使用）
from pylab import *

# 创建一个 8 * 6 点（point）的图，并设置分辨率为 80
figure(figsize=(8,6), dpi=80)

# 创建一个新的 1 * 1 的子图，接下来的图样绘制在其中的第 1 块（也是唯一的一块）
subplot(1,1,1)

X = np.linspace(-np.pi, np.pi, 256,endpoint=True)
C,S = np.cos(X), np.sin(X)

# 绘制余弦曲线，使用蓝色的、连续的、宽度为 1 （像素）的线条
plot(X, C, color="blue", linewidth=1.0, linestyle="-")

# 绘制正弦曲线，使用绿色的、连续的、宽度为 1 （像素）的线条
plot(X, S, color="r", lw=4.0, linestyle="-")

plt.axis([-4,4,-1.2,1.2])
# 设置轴记号

xticks([-np.pi, -np.pi/2, 0, np.pi/2, np.pi],
       [r‘$-\pi$‘, r‘$-\pi/2$‘, r‘$0$‘, r‘$+\pi/2$‘, r‘$+\pi$‘])

yticks([-1, 0, +1],
       [r‘$-1$‘, r‘$0$‘, r‘$+1$‘])
# 在屏幕上显示
show()

ax = gca()
ax.spines[‘right‘].set_color(‘none‘)
ax.spines[‘top‘].set_color(‘none‘)
ax.xaxis.set_ticks_position(‘bottom‘)
ax.spines[‘bottom‘].set_position((‘data‘,0))
ax.yaxis.set_ticks_position(‘left‘)
ax.spines[‘left‘].set_position((‘data‘,0))

plot(X, C, color="blue", linewidth=2.5, linestyle="-", label="cosine")
plot(X, S, color="red",  linewidth=2.5, linestyle="-", label="sine")

legend(loc=‘upper left‘)

# Import necessary packages
import pandas as pd
%matplotlib inline
import matplotlib.pyplot as plt
plt.style.use(‘ggplot‘)
from sklearn import datasets
from sklearn import linear_model
import numpy as np
# Load data
boston = datasets.load_boston()
yb = boston.target.reshape(-1, 1)
Xb = boston[‘data‘][:,5].reshape(-1, 1)
# Plot data
plt.scatter(Xb,yb)
plt.ylabel(‘value of house /1000 ($)‘)
plt.xlabel(‘number of rooms‘)
# Create linear regression object
regr = linear_model.LinearRegression()
# Train the model using the training sets
regr.fit( Xb, yb)
# Plot outputs
plt.scatter(Xb, yb,  color=‘black‘)
plt.plot(Xb, regr.predict(Xb), color=‘blue‘,
         linewidth=3)
plt.show()

t = 2*np.pi/3
# 作一条垂直于x轴的线段，由数学知识可知，横坐标一致的两个点就在垂直于坐标轴的直线上了。这两个点是起始点。
plot([t,t],[0,np.cos(t)], color =‘blue‘, linewidth=2.5, linestyle="--")
scatter([t,],[np.cos(t),], 50, color =‘blue‘)

annotate(r‘$\sin(\frac{2\pi}{3})=\frac{\sqrt{3}}{2}$‘,
         xy=(t, np.sin(t)), xycoords=‘data‘,
         xytext=(+10, +30), textcoords=‘offset points‘, fontsize=16,
         arrowprops=dict(arrowstyle="->", connectionstyle="arc3,rad=.2"))

plot([t,t],[0,np.sin(t)], color =‘red‘, linewidth=2.5, linestyle="--")
scatter([t,],[np.sin(t),], 50, color =‘red‘)

annotate(r‘$\cos(\frac{2\pi}{3})=-\frac{1}{2}$‘,
         xy=(t, np.cos(t)), xycoords=‘data‘,
         xytext=(-90, -50), textcoords=‘offset points‘, fontsize=16,
         arrowprops=dict(arrowstyle="->", connectionstyle="arc3,rad=.2"))

http://matplotlib.org/examples/pylab_examples/axes_demo.html

import matplotlib.pyplot as plt
import numpy as np

# create some data to use for the plot
dt = 0.001
t = np.arange(0.0, 10.0, dt)
r = np.exp(-t[:1000]/0.05)               # impulse response
x = np.random.randn(len(t))
s = np.convolve(x, r)[:len(x)]*dt  # colored noise

# the main axes is subplot(111) by default
plt.plot(t, s)
plt.axis([0, 1, 1.1*np.amin(s), 2*np.amax(s)])
plt.xlabel(‘time (s)‘)
plt.ylabel(‘current (nA)‘)
plt.title(‘Gaussian colored noise‘)

# this is an inset axes over the main axes
a = plt.axes([.65, .6, .2, .2], axisbg=‘y‘)
n, bins, patches = plt.hist(s, 400, normed=1)
plt.title(‘Probability‘)
plt.xticks([])
plt.yticks([])

# this is another inset axes over the main axes
a = plt.axes([0.2, 0.6, .2, .2], axisbg=‘y‘)
plt.plot(t[:len(r)], r)
plt.title(‘Impulse response‘)
plt.xlim(0, 0.2)
plt.xticks([])
plt.yticks([])

plt.show()