清洗数据
用 Python 分析 IoT 数据
Matthias Voppichler
IT Developer
缺失数据
物联网设备缺失数据的原因
- 网络不稳定
- 断电
- 其他外部因素
处理数据质量的时机
- 采集阶段
- 分析阶段
处理缺失数据
处理缺失数据的方法
- 填充
- 均值
- 中位数
- 向前填充
- 向后填充
- 删除
- 停止分析
检测缺失值
df.info()
<class 'pandas.core.frame.DataFrame'>
DatetimeIndex: 12 entries, 2018-10-15 08:00:00 to 2018-10-15 08:55:00
Data columns (total 3 columns):
temperature 8 non-null float64
humidity 8 non-null float64
precipitation 12 non-null float64
dtypes: float64(3)
memory usage: 384.0 bytes
删除缺失值
print(df.head())
temperature humidity precipitation
timestamp
2018-10-15 08:00:00 16.7 64.2 0.0
2018-10-15 08:05:00 16.6 NaN 0.0
2018-10-15 08:10:00 16.5 65.3 0.0
2018-10-15 08:15:00 NaN 65.0 0.0
2018-10-15 08:20:00 16.8 64.3 0.0
df.dropna()
temperature humidity precipitation
timestamp
2018-10-15 08:00:00 16.7 64.2 0.0
2018-10-15 08:10:00 16.5 65.3 0.0
2018-10-15 08:20:00 16.8 64.3 0.0
填充缺失值
df
temperature humidity precipitation
timestamp
2018-10-15 08:00:00 16.7 64.2 0.0
2018-10-15 08:05:00 16.6 NaN 0.0
2018-10-15 08:10:00 17.0 65.3 0.0
2018-10-15 08:15:00 NaN 65.0 0.0
2018-10-15 08:20:00 16.8 64.3 0.0
df.fillna(method="ffill")
temperature humidity precipitation
timestamp
2018-10-15 08:00:00 16.7 64.2 0.0
2018-10-15 08:05:00 16.6 64.2 0.0
2018-10-15 08:10:00 17.0 65.3 0.0
2018-10-15 08:15:00 17.0 65.0 0.0
2018-10-15 08:20:00 16.8 64.3 0.0
测量中断
print(df.head())
timestamp temperature humidity
2018-10-15 00:00:00 13.5 84.7
2018-10-15 00:10:00 13.3 85.6
2018-10-15 00:20:00 12.9 88.8
2018-10-15 00:30:00 12.8 89.2
2018-10-15 00:40:00 13.0 87.7
print(df.isna().sum())
temperature 0
humidity 0
dtype: int64
df_res = df.resample("10min").last()
print(df_res.head())
timestamp temperature humidity
2018-10-15 00:00:00 13.5 84.7
2018-10-15 00:10:00 13.3 85.6
2018-10-15 00:20:00 12.9 88.8
2018-10-15 00:30:00 12.8 89.2
2018-10-15 00:40:00 13.0 87.7
print(df_res.isna().sum())
temperature 34
humidity 34
dtype: int64
测量中断
df_res.plot(title="Environment")
让我们练习吧！
用 Python 分析 IoT 数据
