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《Python for Data Analysis》

绘图和可视化是数据分析中的一项重要工作。通过可视化，能够更好的观察数据的模式，帮助我们找出数据的异常值、必要的数据转换、得出有关模型的想法。

matplotlib

用法：

• 在ipython中，使用ipython --pylab模式启动；

• 或jupyter notebook中，%matplotlib inline (better!)

In [1]: import numpy as np   ...: data = np.arange(10)   ...: data   ...: plt.plot(data)   ...:Out[1]: [
   
    ]
   

Figure对象

In [3]: fig = plt.figure()In [4]: ax1 = fig.add_subplot(2, 2, 1)In [5]: ax2 = fig.add_subplot(2, 2, 2)   ...: ax3 = fig.add_subplot(2, 2, 3)   ...:In [6]: plt.plot(np.random.randn(50).cumsum(), 'k--')Out[6]: [
   
    ]
   

subplots方法

创建一个新的Figure对象，并返回一个含有已创建的subplot对象的Numpy数组。

In [7]: fig, axes = plt.subplots(2, 3)   ...: axes   ...:Out[7]:array([[
   
    ,        
    
     ,        
     
      ],       [
      
       ,        
       
        ,        
        
         ]], dtype=object)
        
       
      
     
    
   

调整subplot周围的间距：subplots_adjust方法

subplots_adjust(left=None, bottom=None, right=None, top=None, wspace=None, hspace=None)

In [8]: fig, axes = plt.subplots(2, 2, sharex=True, sharey=True)   ...: for i in range(2):   ...:     for j in range(2):   ...:         axes[i, j].hist(np.random.randn(500), bins=50, color='k', alpha=0.5)   ...: plt.subplots_adjust(wspace=0, hspace=0)   ...:

颜色、标记和线型

In [9]: plt.figure()   ...: from numpy.random import randn   ...: plt.plot(randn(30).cumsum(), 'ko--')   ...:Out[9]: [
   
    ]
   

In [10]: data = np.random.randn(30).cumsum()    ...: plt.plot(data, 'k--', label='Default')    ...: plt.plot(data, 'k-', drawstyle='steps-post', label='steps-post')    ...: plt.legend(loc='best')    ...:Out[10]: 
   

刻度、标签和图例

In [11]: fig = plt.figure()    ...: ax = fig.add_subplot(1, 1, 1)    ...: ax.plot(np.random.randn(1000).cumsum())    ...: ticks = ax.set_xticks([0, 250, 500, 750, 1000])    ...: labels = ax.set_xticklabels(['one', 'two', 'three', 'four', 'five'],    ...:                             rotation=30, fontsize='small')    ...: ax.set_title('My first matplotlib plot')    ...: ax.set_xlabel('Stages')    ...:Out[11]: 
   

In [12]: from numpy.random import randn    ...: fig = plt.figure(); ax = fig.add_subplot(1, 1, 1)    ...: ax.plot(randn(1000).cumsum(), 'k', label='one')    ...: ax.plot(randn(1000).cumsum(), 'k--', label='two')    ...: ax.plot(randn(1000).cumsum(), 'k.', label='three')    ...: ax.legend(loc='best')    ...:Out[12]: 
   

注解以及在Subplot上绘图

from datetime import datetimefig = plt.figure()ax = fig.add_subplot(1, 1, 1)data = pd.read_csv('examples/spx.csv', index_col=0, parse_dates=True)spx = data['SPX']spx.plot(ax=ax, style='k-')crisis_data = [    (datetime(2007, 10, 11), 'Peak of bull market'),    (datetime(2008, 3, 12), 'Bear Stearns Fails'),    (datetime(2008, 9, 15), 'Lehman Bankruptcy')]for date, label in crisis_data:    ax.annotate(label, xy=(date, spx.asof(date) + 75),                xytext=(date, spx.asof(date) + 225),                arrowprops=dict(facecolor='black', headwidth=4, width=2,                                headlength=4),                horizontalalignment='left', verticalalignment='top')# Zoom in on 2007-2010ax.set_xlim(['1/1/2007', '1/1/2011'])ax.set_ylim([600, 1800])ax.set_title('Important dates in the 2008-2009 financial crisis')