使用pythn的平均值来填充缺失时间

Question

我有一个数据框，缺少一段时间（我希望每分钟）。请参阅下面的示例：

time = np.array([pd.to_datetime("2022-01-01 00:00:00"),pd.to_datetime("2022-01-01 00:00:01"),pd.to_datetime("2022-01-01 00:00:03"), pd.to_datetime("2022-01-01 00:00:04"),pd.to_datetime("2022-01-01 00:00:07"),pd.to_datetime("2022-01-01 00:00:09"), pd.to_datetime("2022-01-01 00:00:10")])
lat = [58.1, 58.4, 58.5, 58.9, 59,59.2, 59.5]
lng = [1.34, 1.44, 1.46, 1.48, 1.55, 1.57, 1.59]

df =  pd.DataFrame({"time": time, "lat": lat, "lng" :lng})


time                lat     lng
2022-01-01 00:00:00 58.1    1.34
2022-01-01 00:00:01 58.4    1.44
2022-01-01 00:00:03 58.5    1.46
2022-01-01 00:00:04 58.9    1.48
2022-01-01 00:00:07 59.0    1.55
2022-01-01 00:00:09 59.2    1.57
2022-01-01 00:00:10 59.5    1.59

我想弥补时间的差距，因此每分钟都有数据的数据，lat/lng填充了平均值之间的值。我的计划是为每分钟创建一系列时间，并尝试使用FFIL或类似的内容来填写缺失点。但是我不知道如何。预期的是，

time                lat     lng
2022-01-01 00:00:00 58.1    1.34
2022-01-01 00:00:01 58.4    1.44
2022-01-01 00:00:01 58.45   1.45
2022-01-01 00:00:03 58.5    1.46
2022-01-01 00:00:04 58.9    1.48
2022-01-01 00:00:05 58.933  1.5033
2022-01-01 00:00:06 58.966  1.5233
2022-01-01 00:00:07 59.0    1.55
2022-01-01 00:00:08 59.1    1.56
2022-01-01 00:00:09 59.2    1.57
2022-01-01 00:00:10 59.5    1.59

请给我一些有关如何执行此操作的建议

Answer 1

创建dateTimeIndex然后添加div。 =“ nofollow noreferrer”> dataFrame.asfreq 和 dataframe.interpaly ：

df = df.set_index('time').asfreq(freq='S').interpolate()
print (df)
                           lat       lng
time                                    
2022-01-01 00:00:00  58.100000  1.340000
2022-01-01 00:00:01  58.400000  1.440000
2022-01-01 00:00:02  58.450000  1.450000
2022-01-01 00:00:03  58.500000  1.460000
2022-01-01 00:00:04  58.900000  1.480000
2022-01-01 00:00:05  58.933333  1.503333
2022-01-01 00:00:06  58.966667  1.526667
2022-01-01 00:00:07  59.000000  1.550000
2022-01-01 00:00:08  59.100000  1.560000
2022-01-01 00:00:09  59.200000  1.570000
2022-01-01 00:00:10  59.500000  1.590000