matplotlib入门--1（条形图， 直方图， 盒须图， 饼图）

作图首先要进行数据的输入，matplotlib包只提供作图相关功能，本身并没有数据读入、输出函数，针对各种试验或统计文本数据输入可以使用numpy提供的数据输入函数。

# -*- coding: gbk -*-
"""
Created on Sun Jan 11 11:17:42 2015

@author: zhang
"""
import numpy as np
import matplotlib.pyplot as plt
import matplotlib as mpl

mpl.rcParams['font.family'] = 'sans-serif'
mpl.rcParams['font.sans-serif'] = [u'SimHei']

#生成数据
dataOut = np.arange(24).reshape(4, 6)
print(dataOut)

#保存数据
np.savetxt('data.txt', dataOut, fmt = '%.1f')

#读取数据
data = np.loadtxt('data.txt')
print(data)

plot 和 bar 函数

# -*- coding: gbk -*-
"""
Created on Sun Jan 11 11:33:14 2015

@author: zhang
"""
import numpy as np
import matplotlib.pyplot as plt
import matplotlib as mpl

mpl.rcParams['font.family'] = 'sans-serif'
mpl.rcParams['font.sans-serif'] = [u'SimHei']

data = np.random.randint(1, 11, 5)
x = np.arange(len(data))

plt.plot(x, data, color = 'r')
plt.bar(x, data, alpha = .5, color = 'g')

plt.show()

结果图片

饼图

# -*- coding: gbk -*-
"""
Created on Sun Jan 11 11:33:14 2015

@author: zhang
"""
import numpy as np
import matplotlib.pyplot as plt
import matplotlib as mpl

mpl.rcParams['font.family'] = 'sans-serif'
mpl.rcParams['font.sans-serif'] = [u'SimHei']

data = np.random.randint(1, 11, 5)
x = np.arange(len(data))

#plt.plot(x, data, color = 'r')
#plt.bar(x, data, alpha = .5, color = 'g')
plt.pie(data, explode = [0,0,.2, 0, 0])

plt.show

在实际工作中经常要对多组数据进行对比分析，这样需要在一个图表里表示出多个数据集。plot函数多数据集表示方法：

# -*- coding: gbk -*-
"""
Created on Sun Jan 11 11:51:41 2015

@author: zhang
"""
import numpy as np
import matplotlib.pyplot as plt
import matplotlib as mpl

mpl.rcParams['font.family'] = 'sans-serif'
mpl.rcParams['font.sans-serif'] = [u'SimHei']

data = np.random.randint(1, 5, (5, 2))
x = np.arange(len(data))

plt.plot(x, data[:, 0], '--', color = 'm')
plt.plot(x, data[:, 1], '-.', color = 'c')

plt.show()

这里用到了matplotlib中defered rendering的概念，它是指在绘图过程中，只有你调用到plt.plot函数是其它的绘图指令才会起效。

也可以通过对条形图的定制实现数据对比，主要有这几种类型 multy bar chart；stack bar chart和back to back bar chart

# -*- coding: gbk -*-
"""
Created on Sun Jan 11 12:03:57 2015

@author: zhang
"""
import numpy as np
import matplotlib.pyplot as plt
import matplotlib as mpl

mpl.rcParams['font.family'] = 'sans-serif'
mpl.rcParams['font.sans-serif'] = [u'SimHei']
mpl.rcParams['axes.unicode_minus'] = False

data = np.random.randint(1, 5, [3, 4])
index = np.arange(data.shape[1])
color_index = ['r', 'g', 'b']

fig, (ax1, ax2, ax3) = plt.subplots(3, 1, figsize = (5, 12))

for i in range(data.shape[0]):
    ax1.bar(index + i*.25 + .1, data[i], width = .25, color = color_index[i],\
    alpha = .5)

for i in range(data.shape[0]):
    ax2.bar(index + .25, data[i], width = .5, color = color_index[i],\
    bottom = np.sum(data[:i], axis = 0), alpha = .7)

ax3.barh(index, data[0], color = 'r', alpha = .5)
ax3.barh(index, -data[1], color = 'b', alpha = .5)

plt.show()
plt.savefig('complex_bar_chart')

统计中常用的两种图标是直方图和盒须图，matplotlib中有针对这两种图表的专门函数：hist和boxplot

# -*- coding: gbk -*-
"""
Created on Sun Jan 11 12:29:34 2015

@author: zhang
"""
import numpy as np
import matplotlib.pyplot as plt
import matplotlib as mpl

mpl.rcParams['font.family'] = 'sans-serif'
mpl.rcParams['font.sans-serif'] = [u'SimHei']

data = np.random.randn(100)

fig, (ax1, ax2) = plt.subplots(1, 2, figsize = (8, 4))

ax1.hist(data)
ax2.boxplot(data)

plt.savefig('hist_boxplot')
plt.show()

本文讲到的所有matplotlib命令都有非常丰富的定制参数，我会在后面文章中讲到，你也可以查看帮助文档学习。