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'] #用來(lái)正常顯示中文標(biāo)簽
plt.rcParams['axes.unicode_minus']=False #用來(lái)正常顯示負(fù)號(hào)

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

plt.tilte('Title in a custom color',color='#123456'）

subplot(111,axisbg=(0.1843,0.3098,0.3098)

%matplotlib inline
import numpy as np
import matplotlib.pyplot as plt
from pylab import *

x = np.arange(-5.0, 5.0, 0.02)
y1 = np.sin(x)

plt.figure(1)
plt.subplot(211)
plt.plot(x, y1)

plt.subplot(212)
#設(shè)置x軸范圍
xlim(-2.5, 2.5)
#設(shè)置y軸范圍
ylim(-1, 1)
plt.plot(x, y1)

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)        # 第一張圖
plt.subplot(211)       # 第一張圖中的第一張子圖
plt.plot([1,2,3])
plt.subplot(212)       # 第一張圖中的第二張子圖
plt.plot([4,5,6])


plt.figure(2)        # 第二張圖
plt.plot([4,5,6])      # 默認(rèn)創(chuàng)建子圖subplot(111)

plt.figure(1)        # 切換到figure 1 ; 子圖subplot(212)仍舊是當(dāng)前圖
plt.subplot(211)       # 令子圖subplot(211)成為figure1的當(dāng)前圖
plt.title('Easy as 1,2,3')  # 添加subplot 211 的標(biāo)題

import numpy as np
import matplotlib.pyplot as plt

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

# 數(shù)據(jù)的直方圖
n, bins, patches = plt.hist(x, 50, normed=1, facecolor='g', alpha=0.75)


plt.xlabel('Smarts')
plt.ylabel('Probability')
#添加標(biāo)題
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()

# 導(dǎo)入 matplotlib 的所有內(nèi)容（nympy 可以用 np 這個(gè)名字來(lái)使用）
from pylab import *

# 創(chuàng)建一個(gè) 8 * 6 點(diǎn)（point）的圖，并設(shè)置分辨率為 80
figure(figsize=(8,6), dpi=80)

# 創(chuàng)建一個(gè)新的 1 * 1 的子圖，接下來(lái)的圖樣繪制在其中的第 1 塊（也是唯一的一塊）
subplot(1,1,1)

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

# 繪制余弦曲線，使用藍(lán)色的、連續(xù)的、寬度為 1 （像素）的線條
plot(X, C, color="blue", linewidth=1.0, linestyle="-")

# 繪制正弦曲線，使用綠色的、連續(xù)的、寬度為 1 （像素）的線條
plot(X, S, color="r", lw=4.0, linestyle="-")

plt.axis([-4,4,-1.2,1.2])
# 設(shè)置軸記號(hào)

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'$+1$'])
# 在屏幕上顯示
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
# 作一條垂直于x軸的線段，由數(shù)學(xué)知識(shí)可知，橫坐標(biāo)一致的兩個(gè)點(diǎn)就在垂直于坐標(biāo)軸的直線上了。這兩個(gè)點(diǎn)是起始點(diǎn)。
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"))

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()

for name,hex in matplotlib.colors.cnames.iteritems():
  print name,hex