I have data from many sensors, and observations come 200 times every second. Now I want to resample at a lower rate, so make the dataset manageable calculation wise. But The time column is absolute and date time. Please see the first column below. Now I want to create an index in absolute datetime so that I can use resample() methods easily to resampling and aggregation at different durations.
Example:
0.000000 1.397081 -0.672387 0.552749
0.005000 2.374832 -0.221770 1.348744
0.010000 3.191852 0.776504 0.044648
0.015000 2.304027 0.188047 0.433253
0.020000 2.331740 -0.000074 0.424112
0.025000 2.869129 0.282714 1.081615
0.030000 3.312915 0.997374 0.456503
0.035000 2.044041 -0.114705 0.993204
I want a method to generate timestamps 200 times a second starting at a timestamp, when this run of experiment was started, 2020/03/14 23:49:19 for example. Starting at 2020/03/14 23:49:19 I want to generate time stamps 200 times every second. This will help me generate a DatetimeIndex and then resample and aggregate it to 10 times a second.
I could find no example at this frequency and granularity, after reading the date functionality pages at pandas, https://pandas.pydata.org/pandas-docs/stable/user_guide/timeseries.html#timestamps-vs-time-spans
the real datafiles are of course extremely big, and confidential so can not post it.
assuming we have for example
df
Out[52]:
t v1 v2 v3
0 0.000 1.397081 -0.672387 0.552749
1 0.005 2.374832 -0.221770 1.348744
2 0.010 3.191852 0.776504 0.044648
3 0.015 2.304027 0.188047 0.433253
4 0.020 2.331740 -0.000074 0.424112
5 0.025 2.869129 0.282714 1.081615
6 0.030 3.312915 0.997374 0.456503
7 0.035 2.044041 -0.114705 0.993204
we can define a start date/time and add the existing time axis as a timedelta (assuming seconds here) and set that as index:
start = pd.Timestamp("2020/03/14 23:49:19")
df.index = pd.DatetimeIndex(start + pd.to_timedelta(df['t'], unit='s'))
df
Out[55]:
t v1 v2 v3
t
2020-03-14 23:49:19.000 0.000 1.397081 -0.672387 0.552749
2020-03-14 23:49:19.005 0.005 2.374832 -0.221770 1.348744
2020-03-14 23:49:19.010 0.010 3.191852 0.776504 0.044648
2020-03-14 23:49:19.015 0.015 2.304027 0.188047 0.433253
2020-03-14 23:49:19.020 0.020 2.331740 -0.000074 0.424112
2020-03-14 23:49:19.025 0.025 2.869129 0.282714 1.081615
2020-03-14 23:49:19.030 0.030 3.312915 0.997374 0.456503
2020-03-14 23:49:19.035 0.035 2.044041 -0.114705 0.993204
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