Pandas resample timeseries data to 15 mins and 45 mins - using multi-index or column

Question

I have some timeseries data as a Pandas dataframe which starts off with observations at 15 mins past the hour and 45 mins past (time intervals of 30 mins) then changes frequency to every minute. I want to resample the data so that it has a regular frequency of every 30 minutes, at 15 past and 45 past the hours for the whole dataframe.

I thought of two ways of achieving this.
1. Simply filter the dataframe for all observations at 15min and 45min, using the time-series data as a column in the dataframe.
2. Re-set the index so the time-series data is part of a multi-index (the 0th level of the index is the weather station, the 1st level is the time of the observation) and use the Pandas date-time timeseries functionality such as resample().

The original dataframe, weather, looks like this:

                  parsed_time           Pressure  Temp    Hum
Station   (index)   
Bow       1        2018-04-15 14:15:00   1012     20.0    87
          2        2018-04-15 14:45:00   1013     20.0    87
          3        2018-04-15 15:15:00   1012     21.0    87
          4        2018-04-15 15:45:00   1014     22.0    86
          5        2018-04-15 16:00:00   1015     22.0    86
          6        2018-04-15 16:01:00   1012     25.0    86
          7        2018-04-15 16:02:00   1012     25.0    86
Stratford 8        2018-04-15 14:15:00   1011     18.0    87
          9        2018-04-15 14:45:00   1011     18.0    87
          10       2018-04-15 15:15:00   1012     18.0    87
          11       2018-04-15 15:45:00   1014     19.0    86
          12       2018-04-15 16:00:00   1014     19.0    86
          13       2018-04-15 16:01:00   1015     19.0    86
          14       2018-04-15 16:02:00   1016     20.0    86
          15       2018-04-15 16:04:00   1016     20.0    86

With method 1, I have the problem that my boolean select operations don't seem to work as expected. For example

weather_test = weather[weather['parsed_time'].dt.minute == (15 & 45)]

gives parsed_time values like this:

2018-04-15 14:13:00
2018-04-15 15:13:00

weather_test = weather[weather['parsed_time'].dt.minute == (15 | 45)]

results in parsed_time values like this:

2018-04-15 14:47:00
2018-04-15 14:47:00

I can't find anything in the docs to explain this behaviour. What I want is pressure, temp, humidity by station at the following times:

2018-04-15 14:45:00    
2018-04-15 15:15:00  
2018-04-15 15:45:00
2018-04-15 16:15:00

and so on.

With method 2, I thought of resampling the data so that observations for which I have minute-by-minute data are replaced by the mean of the previous 30 minutes. This functionality only seems to work if the parsed_time column is part of the index, so I used the following code to set the parsed_time as part of a multi-index:

weather.set_index('parsed_time', append=True, inplace=True)
weather.index.set_names('station', level=0, inplace=True)
weather = weather.reset_index(level=1, drop=True)

to end up with a dataframe that looks like this:

                                  Pressure   Temp    Hum
Station    parsed_time
Bow            2018-04-15 14:15:00    1012       20.0    87
           2018-04-15 14:45:00    1013       20.0    87
           2018-04-15 15:15:00    1012       21.0    87
           2018-04-15 15:45:00    1014       22.0    86
           2018-04-15 16:00:00    1015       22.0    86
           2018-04-15 16:01:00    1012       25.0    86
           2018-04-15 16:02:00    1012       25.0    86
Stratford  2018-04-15 14:15:00    1011       18.0    87
           2018-04-15 14:45:00    1011       18.0    87
           2018-04-15 15:15:00    1012       18.0    87
           2018-04-15 15:45:00    1014       19.0    86
           2018-04-15 16:00:00    1014       19.0    86
           2018-04-15 16:01:00    1015       19.0    86
           2018-04-15 16:02:00    1016       20.0    86
           2018-04-15 16:04:00    1016       20.0    86

Note that the sampling of observations varies from every 30 minutes at :15 past and :45 past to every minute (e.g. :01, :02, :14, etc), and it also varies by station - not all stations have every observation.

I tried this:

weather_test = weather.resample('30min', level=1).mean()

but this resamples without an offset and also gets rid of the station level in the multi-index.

The desired result is this:

                              Pressure   Temp    Hum
Station    parsed_time
Bow            2018-04-15 14:15:00    1012       20.0    87
           2018-04-15 14:45:00    1013       20.0    87
           2018-04-15 15:15:00    1012       21.0    87
           2018-04-15 15:45:00    1014       22.0    86
           2018-04-15 16:15:00    1013       24.0    86
Stratford  2018-04-15 14:15:00    1011       18.0    87
           2018-04-15 14:45:00    1011       18.0    87
           2018-04-15 15:15:00    1012       18.0    87
           2018-04-15 15:45:00    1014       19.0    86
           2018-04-15 16:15:00    1015       19.5    86

where the minute-by-minute observations have been resampled as the mean over a 30-minute interval at :15 and :45 past the hour.

Keeping the station as a level in the multi-index is essential. I can't use the time index as an index on its own as the values repeat for each station (and are not unique).

All help appreciated as I have been going round in circles with this one for a while now. Thanks!

I have looked at quite a few previous posts including: Boolean filter using a timestamp value on a dataframe in Python
How do I round datetime column to nearest quarter hour
and: Resampling a pandas dataframe with multi-index containing timeseries which seems a bit complicated for something that should be quite simple ...

and the docs: http://pandas.pydata.org/pandas-docs/stable/generated/pandas.DataFrame.resample.html Thanks!

chuni0r · Accepted Answer · 2018-08-07T10:30:56.743

Starting from your second last dataframe (after using weather.reset_index(Station, inplace=True)):

                           Station  Pressure  Temp   Hum
parsed_time                                         
2018-04-15 14:15:00        Bow    1012.0  20.0  87.0
2018-04-15 14:45:00        Bow    1013.0  20.0  87.0
2018-04-15 15:15:00        Bow    1012.0  21.0  87.0
2018-04-15 15:45:00        Bow    1014.0  22.0  86.0
2018-04-15 16:00:00        Bow    1015.0  22.0  86.0
2018-04-15 16:01:00        Bow    1012.0  25.0  86.0
2018-04-15 16:02:00        Bow    1012.0  25.0  86.0
2018-04-15 14:15:00  Stratford    1011.0  18.0  87.0
2018-04-15 14:45:00  Stratford    1011.0  18.0  87.0
2018-04-15 15:15:00  Stratford    1012.0  18.0  87.0
2018-04-15 15:45:00  Stratford    1014.0  19.0  86.0
2018-04-15 16:00:00  Stratford    1014.0  19.0  86.0
2018-04-15 16:01:00  Stratford    1015.0  19.0  86.0
2018-04-15 16:02:00  Stratford    1016.0  20.0  86.0
2018-04-15 16:04:00  Stratford    1016.0  20.0  86.0

you could use a combination of groupby and resample:

res = weather.groupby('Station').resample('30min').mean().reset_index('Station')

By default, resample chooses the bin intervals [16:00, 16:30) and [16:30, 17:00). As you already noticed, the time index is resampled without an offset, but you can add it back afterwards using DateOffset:

res.index = res.index + pd.DateOffset(minutes=15)

which yields:

                           Station  Pressure  Temp   Hum
parsed_time                                         
2018-04-15 14:15:00        Bow   1012.00  20.0  87.0
2018-04-15 14:45:00        Bow   1013.00  20.0  87.0
2018-04-15 15:15:00        Bow   1012.00  21.0  87.0
2018-04-15 15:45:00        Bow   1014.00  22.0  86.0
2018-04-15 16:15:00        Bow   1013.00  24.0  86.0
2018-04-15 14:15:00  Stratford   1011.00  18.0  87.0
2018-04-15 14:45:00  Stratford   1011.00  18.0  87.0
2018-04-15 15:15:00  Stratford   1012.00  18.0  87.0
2018-04-15 15:45:00  Stratford   1014.00  19.0  86.0
2018-04-15 16:15:00  Stratford   1015.25  19.5  86.0

Alternatively, you could specifiy the offset directly in the resample method:

weather.groupby('Station').resample('30min', loffset=pd.Timedelta('15min')).mean()

Thanks, that works beautifully! The only thing I would add to make it clearer is that by ```reset_index('Station')```, I believe you mean ```reset_index('Station', append=True, inplace=True)``` which adds the station as a column instead of being part of the index. Is this correct? — LucieCBurgess, Aug 07 '18 at 10:16
Yes, thats correct. I think, `append=True` is not necessary. — chuni0r, Aug 07 '18 at 10:24

score 1 · Answer 2 · answered Aug 06 '18 at 11:51

1

I don't have your data so I cannot examine this directly, but try the following syntax for the option you refer to as option 1:

weather_test = weather[(weather['parsed_time'].dt.minute == 15) | (weather['parsed_time'].dt.minute == 45)]

answered Aug 06 '18 at 11:51

BossaNova

1,509
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Thanks @BossaNova - that fixes option 1 but I'm still very interested in being able to resample the datapoints which aren't at equal intervals – LucieCBurgess Aug 06 '18 at 13:28

score 1 · Answer 3 · answered Aug 06 '18 at 17:02

1

If you start off without any index (except for a row index), you can perform the following:

# Create a rounded timestamp
df['parsed_time_rounded'] = (df['parsed_time'] - pd.Timedelta('15min')).dt.round('30min') + pd.Timedelta('15min')
# Group by the station, and the rounded timestamp instead of the raw timestamp
df.groupby(['Station', 'parsed_time_rounded']).mean()

answered Aug 06 '18 at 17:02

PMende

5,171
2
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Thanks PMende - this was actually extremely useful for rounding timestamps in another dataframe to match the weather station format, and is actually the perfect answer to this question here, which I also perused: https://stackoverflow.com/questions/49575973/round-a-pandas-timestamp-using-an-offset – LucieCBurgess Aug 07 '18 at 13:57

Pandas resample timeseries data to 15 mins and 45 mins - using multi-index or column

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