繪圖和可視化（matplotlib）

《Python for Data Analysis》

繪圖和可視化是數據分析中的一項重要工作。通過可視化，能夠更好的觀察數據的模式，幫助我們找出數據的異常值、必要的數據轉換、得出有關模型的想法。

matplotlib

用法：

• 在ipython中，使用ipython --pylab模式啓動；

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

In [1]: import numpy as np
   ...: data = np.arange(10)
   ...: data
   ...: plt.plot(data)
   ...:
Out[1]: [<matplotlib.lines.Line2D at 0x70b6c18>]

Figure對象

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]: [<matplotlib.lines.Line2D at 0xe404ba8>]

subplots方法

創建一個新的Figure對象，並返回一個含有已創建的subplot對象的Numpy數組。

In [7]: fig, axes = plt.subplots(2, 3)
   ...: axes
   ...:
Out[7]:
array([[<matplotlib.axes._subplots.AxesSubplot object at 0x000000000E363588>,
        <matplotlib.axes._subplots.AxesSubplot object at 0x000000000E598198>,
        <matplotlib.axes._subplots.AxesSubplot object at 0x000000000E693B00>],
       [<matplotlib.axes._subplots.AxesSubplot object at 0x000000000E6F67F0>,
        <matplotlib.axes._subplots.AxesSubplot object at 0x000000000E8065F8>,
        <matplotlib.axes._subplots.AxesSubplot object at 0x000000000E851EB8>]], dtype=object)

參數	選項
nrows	subplot的行數
ncols	subplot的列數
sharex	所有subplot使用相同的X軸刻度（調節xlim會影響所有subplot）
sharey	共享Y軸刻度
subplot_kw	用於創建各subplot的關鍵字字典
**fig_kw	創建figure的其他關鍵字，如plot.subplots(2,2,figuresize=(8,6))

調整subplot周圍的間距：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)
   ...:

顏色、標記和線型

In [9]: plt.figure()
   ...: from numpy.random import randn
   ...: plt.plot(randn(30).cumsum(), 'ko--')
   ...:
Out[9]: [<matplotlib.lines.Line2D at 0x10c49b00>]

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]: <matplotlib.legend.Legend at 0x10dd6ef0>

刻度、標籤和圖例

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]: <matplotlib.text.Text at 0x10e6c3c8>

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]: <matplotlib.legend.Legend at 0x111d06d8>

註解以及在Subplot上繪圖

from datetime import datetime

fig = 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 2007-2010
ax.set_xlim(['1/1/2007', '1/1/2011'])
ax.set_ylim([600, 1800])

ax.set_title('Important dates in the 2008-2009 financial crisis')