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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7.9
处理时间序列中的缺失值
｜
123
延伸阅读
y
pandas
文档 ：滚动窗口（
http://bit.ly/2HTIicA
）
y
《什么是移动平均或平滑技术》（
http://bit.ly/2HRRHBn
）
7.9
处理时间序列中的缺失值
问题描述
处理时间序列数据中的缺失值。
解决方案
除了之前讨论过的针对缺失数据的处理策略，针对时间序列数据，还可以使用插值法来
填充由缺失值造成的数据缺口 ：
#
加载库
import pandas as pd
import numpy as np
#
创建日期
time_index = pd.date_range("01/01/2010", periods=5, freq="M")
#
创建数据帧，设置索引
dataframe = pd.DataFrame(index=time_index)
#
创建带缺失数据的特征
dataframe["Sales"] = [1.0,2.0,np.nan,np.nan,5.0]
#
对缺失数据进行插值
dataframe.interpolate()
Sales
2010-01-31 1.0
2010-02-28 2.0
2010-03-31 3.0
2010-04-30 4.0
2010-05-31 5.0
或者，可以用前面的值来替换缺失值（也就是向前填充）：
#
向前填充
dataframe.ffill()
124
｜
第
7
章
处理日期和时间
Sales
2010-01-31 1.0
2010-02-28 2.0
2010-03-31 ...
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