filling in the missing times using average for the values pythn

user_17035737 0
Viewed 34

I have this dataframe with some time missing (I want it to be every minute). Please see the sample below:

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

I want to fill out the gaps in time so there is data for every minute annd the lat/lng is filled in with an average of the values in between. my plan was to create an array of time for each minute and try using ffil or something similar to fill out the missing points. But I cannot figure out how. The expected out put would be this

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

Please give me some advice on how to do this

imputation pandas dataframe datetime python
1 Answers

Create DatetimeIndex then add missing times by div.DataFrame.asfreq and interpolate by DataFrame.interpolate:

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
user_29010020
Related