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.sans-serif']=['SimHei'] #ÓÃÀ´Õý³£ÏÔʾÖÐÎıêÇ©
plt.rcParams['axes.unicode_minus']=False #ÓÃÀ´Õý³£ÏÔʾ¸ººÅ

%matplotlib inline #notebookģʽÏÂ

%pylab inline #ipythonģʽÏÂ

%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)
#ÉèÖÃxÖ᷶Χ
xlim(-2.5, 2.5)
#ÉèÖÃyÖ᷶Χ
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) # µÚÒ»ÕÅͼ
plt.subplot(211) # µÚÒ»ÕÅͼÖеĵÚÒ»ÕÅ×Óͼ
plt.plot([1,2,3])
plt.subplot(212) # µÚÒ»ÕÅͼÖеĵڶþÕÅ×Óͼ
plt.plot([4,5,6])
plt.figure(2) # µÚ¶þÕÅͼ
plt.plot([4,5,6]) # ĬÈÏ´´½¨×Óͼsubplot(111)
plt.figure(1) # Çл»µ½figure 1 ; ×Óͼsubplot(212)ÈÔ¾ÉÊǵ±Ç°Í¼
plt.subplot(211) # Áî×Óͼsubplot(211)³ÉΪfigure1µÄµ±Ç°Í¼
plt.title('Easy as 1,2,3') # Ìí¼Ósubplot 211 µÄ±êÌâ

import numpy as np
import matplotlib.pyplot as plt
mu, sigma = 100, 15
x = mu + sigma * np.random.randn(10000)
# Êý¾ÝµÄÖ±·½Í¼
n, bins, patches = plt.hist(x, 50, normed=1, facecolor='g', alpha=0.75)
plt.xlabel('Smarts')
plt.ylabel('Probability')
#Ìí¼Ó±êÌâ
plt.title('Histogram of IQ')
#Ìí¼ÓÎÄ×Ö
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()

# µ¼Èë matplotlib µÄËùÓÐÄÚÈÝ£¨nympy ¿ÉÒÔÓà np Õâ¸öÃû×ÖÀ´Ê¹Óã©
from pylab import *
# ´´½¨Ò»¸ö 8 * 6 µã£¨point£©µÄͼ£¬²¢ÉèÖ÷ֱæÂÊΪ 80
figure(figsize=(8,6), dpi=80)
# ´´½¨Ò»¸öÐ嵀 1 * 1 µÄ×Óͼ£¬½ÓÏÂÀ´µÄͼÑù»æÖÆÔÚÆäÖÐµÄµÚ 1 ¿é£¨Ò²ÊÇΨһµÄÒ»¿é£©
subplot(1,1,1)
X = np.linspace(-np.pi, np.pi, 256,endpoint=True)
C,S = np.cos(X), np.sin(X)
# »æÖÆÓàÏÒÇúÏߣ¬Ê¹ÓÃÀ¶É«µÄ¡¢Á¬ÐøµÄ¡¢¿í¶ÈΪ 1 £¨ÏñËØ£©µÄÏßÌõ
plot(X, C, color="blue", linewidth=1.0, linestyle="-")
# »æÖÆÕýÏÒÇúÏߣ¬Ê¹ÓÃÂÌÉ«µÄ¡¢Á¬ÐøµÄ¡¢¿í¶ÈΪ 1 £¨ÏñËØ£©µÄÏßÌõ
plot(X, S, color="r", lw=4.0, linestyle="-")
plt.axis([-4,4,-1.2,1.2])
# ÉèÖÃÖá¼ÇºÅ
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'$+1$'])
# ÔÚÆÁÄ»ÉÏÏÔʾ
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
# ×÷Ò»Ìõ´¹Ö±ÓÚxÖáµÄÏ߶Σ¬ÓÉÊýѧ֪ʶ¿ÉÖª£¬ºá×ø±êÒ»ÖµÄÁ½¸öµã¾ÍÔÚ´¹Ö±ÓÚ×ø±êÖáµÄÖ±ÏßÉÏÁË¡£ÕâÁ½¸öµãÊÇÆðʼµã¡£
plot([t,t],[0,np.cos(t)], color ='blue', linewidth=2.5, linestyle="--")
scatter([t,],[np.cos(t),], 50, color ='blue')
annotate(r'$sin(frac{2pi}{3})=frac{sqrt{3}}{2}$',
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'$cos(frac{2pi}{3})=-frac{1}{2}$',
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()

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

autopct enables you to display the percent value using Python string formatting. For example, if autopct='%.2f', then for each pie wedge, the format string is '%.2f' and the numerical percent value for that wedge is pct, so the wedge label is set to the string '%.2f'%pct.