July 2018
Intermediate to advanced
491 pages
9h 39m
Chinese
Content preview from 利用 Python 进行数据分析（原书第2版）
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In [34]: plt.plot(data, 'k--', label='Default')
Out[34]: [<matplotlib.lines.Line2D at 0x7fb624d86160>]
In [35]: plt.plot(data, 'k-', drawstyle='steps-post', label='steps-post')
Out[35]: [<matplotlib.lines.Line2D at 0x7fb624d869e8>]
In [36]: plt.legend(loc='best')
图 9-7：不同 drawstyle 选项下的折线图
你可能会注意到在运行代码后会有像 <matplotlib.lines.Line2D at ...> 这样
的输出。matplotlib 返回的对象引用了刚刚添加的图表子组件。很多时候你可以安全地忽
略这些输出。这里，由于我们向 plot 传递了 label，我们可以使用 plt.legend 为
每条线生成一个用于区分的图例。
无论你在用数据绘图时是否传递了 lebel 选项，你都必须调用 plt.legend
（如果你有轴的引用，也可以用 ax.legend）来生成图例。
9.1.3 刻度、标签和图例
对于大多数图表修饰工作，有两种主要的方式：使用程序性的pyplot 接 口（即
matplotlib.pyplot）和更多面向对象的原生 matplotlib API。
pyplot 接口设计为交互式使用，包含了像
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