July 2017
Intermediate to advanced
398 pages
11h 54m
Chinese
Content preview from Python数据处理
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208
｜
第
10
章
报》发布过一个非常棒的关于疫苗和发病率的时间序列（
http://graphics.wsj.com/infectious-
diseases-and-vaccines/
）。交互元素允许你进行探索，内置的延时移动画创造了一个易读的画
面。疫苗引进标记也增加了阅读的清晰度。
我们还没有研究数据集随时间推移的变化。下一步操作最好是收集过去几年的数据集。这
些数据可以回答类似于这样的问题：随时间推移什么地方的童工数量在增长？是否能够看
到随时间推移的一个明显的地区趋势？当合并另一个数据集，是否能够看到其他的趋势
（例如，童工雇用数量是否伴随农业出口增长）？
在
Stack Overflow
（
http://stackoverflow.com/questions/19079143/how-to-plot-time-series-in-
python
）上有一个很好的回答，提供了更多的关于使用
matplotlib
来绘制时间序列数据图
表的信息。还记得在第
9
章中
agate
表的
rows
和
columns
方法可以用来选择一列或者一行
的数据吗？这两个方法返回的列表可以被传入任何
matplotlib
函数，把数据传入到图表
中。
如果你想要了解如何使用
Bokeh
处理时间相关数据，可以查看它们很棒的实例（
http://
bokeh.pydata.org/en/latest/docs/user_guide/charts.html#timeseries
）。
2.
时间线数据
时间线数据可以帮助你向听众介绍某主题历史中重要的时刻，或者最近发展中的转折。例 ...
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I'm always learning. So when I got on to O'Reilly, I was like a kid in a candy store. There are playlists. There are answers. There's on-demand training. It's worth its weight in gold, in terms of what it allows me to do.