Python 将df重新采样至微秒-秒
我试图在df中重新采样时间戳,以每0.1秒显示一次。这是重新采样,但我丢失了与这些时间戳相关的数据Python 将df重新采样至微秒-秒,python,pandas,Python,Pandas,我试图在df中重新采样时间戳,以每0.1秒显示一次。这是重新采样,但我丢失了与这些时间戳相关的数据 d = ({ 'Time' : ['2010-07-27 09:25:31.1','2010-07-27 09:25:31.2','2010-07-27 09:25:31.4','2010-07-27 09:25:31.5','2010-07-27 09:25:31.6','2010-07-27 09:25:31.9','2010-07-27 09:25:32.0'], 'A
d = ({
'Time' : ['2010-07-27 09:25:31.1','2010-07-27 09:25:31.2','2010-07-27 09:25:31.4','2010-07-27 09:25:31.5','2010-07-27 09:25:31.6','2010-07-27 09:25:31.9','2010-07-27 09:25:32.0'],
'Area' : ['A','A','A','A','A','A','A',],
})
df = pd.DataFrame(data=d)
df = df.set_index('Time').asfreq('0.1S').reset_index()
输出:
预期:
0 2010-07-27 09:25:31.100 A
1 2010-07-27 09:25:31.200 A
2 2010-07-27 09:25:31.300 NaN
3 2010-07-27 09:25:31.400 A
4 2010-07-27 09:25:31.500 A
5 2010-07-27 09:25:31.600 A
6 2010-07-27 09:25:31.700 Nan
7 2010-07-27 09:25:31.800 NaN
8 2010-07-27 09:25:31.900 A
9 2010-07-27 09:25:32.000 A
你可以这样做。
首先,您需要确保列位于datetime64[ns]中,然后对该列使用重采样
df['Time']=pd.to_datetime(df['Time'])
df.set_index('Time').resample('0.1S').last().fillna(0).reset_index()
输出
df['Time']=pd.to_datetime(df['Time'])
df.set_index('Time').resample('0.1S').last().fillna(0).reset_index()
Time Area
0 2010-07-27 09:25:31.100 A
1 2010-07-27 09:25:31.200 A
2 2010-07-27 09:25:31.300 0
3 2010-07-27 09:25:31.400 A
4 2010-07-27 09:25:31.500 A
5 2010-07-27 09:25:31.600 A
6 2010-07-27 09:25:31.700 0
7 2010-07-27 09:25:31.800 0
8 2010-07-27 09:25:31.900 A
9 2010-07-27 09:25:32.000 A