July 2019
Intermediate to advanced
365 pages
8h 13m
Chinese
Content preview from Python机器学习手册：从数据预处理到深度学习
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7.8
使用滚动时间窗口
｜
121
#
创建数据
dataframe["dates"] = pd.date_range("1/1/2001", periods=5, freq="D")
dataframe["stock_price"] = [1.1,2.2,3.3,4.4,5.5]
#
让值滞后一行
dataframe["previous_days_stock_price"] = dataframe["stock_price"].shift(1)
#
查看数据帧
dataframe
dates stock_price previous_days_stock_price
0 2001-01-01 1.1 NaN
1 2001-01-02 2.2 1.1
2 2001-01-03 3.3 2.2
3 2001-01-04 4.4 3.3
4 2001-01-05 5.5 4.4
讨论
通常情况下，数据之间的时间间隔是固定的（例如，一天、一小时、三小时），而我们
又常常对使用历史数据做预测（这被称为
滞后
一个特征）感兴趣。例如，使用前一天的
股票价格来预测今天的价格。有了
pandas
，我们可以使用
shift
让数值滞后一行，创建
一个包含历史数值的新特征。
在解决方案中，因为没有前一天的
stock_price
值，所以
previous_days_stock_price
的第一行是一个缺失值。
7.8
使用滚动时间窗口
问题描述
计算一个时间序列数据针对某个滚动时间的统计量。
解决方案
#
加载库
import pandas ...
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