Matplotlib——高级图

图是用来更好的解释数据

目标：让老板或客户印象深刻，一目了然

In [1]:

%matplotlib inline

1. 饼状图

In [61]:

import matplotlib.pyplot as plt

​

plt.figure(figsize=(9,6))

​

# The slices will be ordered and plotted counter-clockwise.

labels = [u'直接访问', u'外部链接', u'搜索引擎']

sizes = [160, 130, 110]

colors = ['yellowgreen', 'gold', 'lightskyblue']

​

#explode 爆炸出来

explode = (0.05, 0.0, 0.0)  

​

patches, l_texts, p_texts = plt.pie(sizes, explode=explode, labels=labels, colors=colors, labeldistance=0.8,

        autopct='%3.1f%%', shadow=True, startangle=90, pctdistance=0.6)

​

# 设置x，y轴刻度一致，这样饼图才能是圆的

#plt.axis('equal')

#plt.legend()

​

"""

for t in l_texts:

    t.set_size(20)

​

for t in p_texts:

    t.set_size(20)

"""

plt.show()

2. 柱状图

In [70]:

import numpy as np

from matplotlib import pyplot as plt

​

plt.figure(figsize=(9,6))

​

n = 12

X = np.arange(n)+1

# numpy.random.uniform(low=0.0, high=1.0, size=None), normal

Y1 = (1-X/float(n+1)) * np.random.uniform(0.5,1.0,n)

Y2 = (1-X/float(n+1)) * np.random.uniform(0.5,1.0,n)

​

# bar and barh

width = 0.5

plt.bar(X, Y1, width=width, facecolor='#9999ff', edgecolor='white')

#plt.bar(X, -Y2, width=width, facecolor='#ff9999', edgecolor='white')

​

"""

for x,y in zip(X,Y1):

    plt.text(x+0.4, y+0.05, '%.2f' % y, ha='center', va= 'bottom')

for x,y in zip(X,-Y2):

    plt.text(x+0.4, y-0.15, '%.2f' % y, ha='center', va= 'bottom')

"""

​

#plt.ylim(-1.25,+1.25)

plt.show()

3. 散点图

In [74]:

from matplotlib import pyplot as plt

import numpy as np

​

plt.figure(figsize=(9,6))

​

n = 1024

​

# rand 和 randn

X = np.random.randn(1,n)

Y = np.random.randn(1,n)

​

T = np.arctan2(Y,X)

​

plt.scatter(X,Y, s=75, c=T, alpha=.4, marker='o')

​

#plt.xlim(-1.5,1.5), plt.xticks([])

#plt.ylim(-1.5,1.5), plt.yticks([])

​

plt.show()

4. 概率分布

In [79]:

from matplotlib import pyplot as plt

import numpy as np

​

mu = 0

sigma = 1

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

​

fig,(ax0,ax1)=plt.subplots(ncols=2, figsize=(9,6))

​

ax0.hist(x, 20, normed=1, histtype='bar', facecolor='g', alpha=0.75)

ax0.set_title('pdf')

​

ax1.hist(x, 20, normed=1, histtype='bar', rwidth=0.8, cumulative=True)

ax1.set_title('cdf')

​

plt.show()

5.组合图

In [81]:

# ref : http://matplotlib.org/examples/pylab_examples/scatter_hist.html

​

import numpy as np

import matplotlib.pyplot as plt

​

# the random data

x = np.random.randn(1000)

y = np.random.randn(1000)

​

# 定义子图区域

left, width = 0.1, 0.65

bottom, height = 0.1, 0.65

bottom_h = left_h = left + width + 0.02

​

rect_scatter = [left, bottom, width, height]

rect_histx = [left, bottom_h, width, 0.2]

rect_histy = [left_h, bottom, 0.2, height]

​

plt.figure(1, figsize=(6, 6))

​

# 根据子图区域来生成子图

axScatter = plt.axes(rect_scatter)

axHistx = plt.axes(rect_histx)

axHisty = plt.axes(rect_histy)

​

# no labels

#axHistx.xaxis.set_ticks([])

#axHisty.yaxis.set_ticks([])

​

# now determine nice limits by hand:

N_bins=20

xymax = np.max([np.max(np.fabs(x)), np.max(np.fabs(y))])

binwidth = xymax/N_bins

lim = (int(xymax/binwidth) + 1) * binwidth

nlim = -lim

​

# 画散点图，概率分布图

axScatter.scatter(x, y)

axScatter.set_xlim((nlim, lim))

axScatter.set_ylim((nlim, lim))

​

bins = np.arange(nlim, lim + binwidth, binwidth)

axHistx.hist(x, bins=bins)

axHisty.hist(y, bins=bins, orientation='horizontal')

​

# 共享刻度

axHistx.set_xlim(axScatter.get_xlim())

axHisty.set_ylim(axScatter.get_ylim())

​

plt.show()

In [7]:

# ref http://matplotlib.org/examples/showcase/integral_demo.html

import numpy as np

import matplotlib.pyplot as plt

​

def func(x):

    return (x - 3) * (x - 5) * (x - 7) + 85

​

a, b = 2, 9  # integral limits

x = np.linspace(0, 10)

y = func(x)

​

# 画线

fig, ax = plt.subplots()

plt.plot(x, y, 'r', linewidth=2)

plt.ylim(ymin=0)

​

# 画阴影区域

xf = x[np.where((x>a)&(x<b))]

plt.fill_between(xf, np.zeros(len(xf)), func(xf), color='blue', alpha=.25)

​

# 画文本

plt.text(0.5 * (a + b), 30, r"$\int_a^b f(x)\mathrm{d}x$",

         horizontalalignment='center', fontsize=20)

​

plt.show()

6. 三维数据图

In [91]:

import numpy as np

import matplotlib.pyplot as plt

​

fig = plt.figure(figsize=(9,6),facecolor='white')

​

# Number of ring

n = 50

size_min = 50

size_max = 50*50

​

# Ring position

P = np.random.rand(n,2)

​

# Ring colors R,G,B,A

C = np.ones((n,4)) * (0,0,0,1)

# Alpha color channel goes from 0 (transparent) to 1 (opaque)

C[:,3] = np.linspace(0,1,n)

​

# Ring sizes

S = np.linspace(size_min, size_max, n)

​

# Scatter plot

plt.scatter(P[:,0], P[:,1], s=S, lw = 0.5,

                  edgecolors = C, facecolors='None')

​

plt.xlim(0,1), plt.xticks([])

plt.ylim(0,1), plt.yticks([])

​

plt.show()

In [88]:

from mpl_toolkits.mplot3d import Axes3D

import numpy as np

import matplotlib.pyplot as plt

​

fig = plt.figure(figsize=(9,6))

ax = fig.add_subplot(111,projection='3d')

​

z = np.linspace(0, 6, 1000)

r = 1

x = r * np.sin(np.pi*2*z)

y = r * np.cos(np.pi*2*z)

​

ax.plot(x, y, z, label=u'螺旋线', c='r')

ax.legend()

​

# dpi每英寸长度的点数

plt.savefig('3d_fig.png',dpi=200)

plt.show()

3d画图种类很多，可参考：http://matplotlib.org/mpl_toolkits/mplot3d/tutorial.html

其他种类图可参考：http://matplotlib.org/gallery.html

7. 美化

In [ ]:

import seaborn as sns

In [10]:

print plt.style.available #ggplot, bmh, dark_background, fivethirtyeight, grayscale

#plt.style.use('bmh')

[u'seaborn-darkgrid', u'seaborn-notebook', u'classic', u'seaborn-ticks', u'grayscale', u'bmh',