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I have a pandas.core.series.TimeSeries named ts like this:
timestamp
2013-08-11 14:23:50 0.3219
2013-08-11 14:23:49 0.3222
2013-08-11 14:19:14 0.3305
2013-08-11 00:47:15 0.3400
2013-08-11 00:47:15.001 0.3310
2013-08-11 00:47:15.002 0.3310
2013-08-10 22:38:15.003 0.3400
2013-08-10 22:38:14 0.3403
2013-08-10 22:38:13 0.3410
Index of this TimeSerie are irregularly spaced.
I would like to have value of ts for a given datetime such as 2013-08-11 14:20:00
I just need to interpolate ONE value, not the whole TimeSerie
I just want to interpolate data using a linear function between the previous index (2013-08-11 14:23:49) and the next index (2013-08-11 14:19:14)
406
These methods use the numerical values of the index. Both 'polynomial' and 'spline' require that you also specify an order (int), e.g. df. interpolate(method='polynomial', order=5). 'krogh', 'piecewise_polynomial', 'spline', 'pchip', 'akima': Wrappers around the SciPy interpolation methods of similar names.
You can interpolate missing values ( NaN ) in pandas. DataFrame and Series with interpolate() . This article describes the following contents. Use dropna() and fillna() to remove missing values NaN or to fill them with a specific value.
Linear interpolation is the most straightforward and commonly used interpolation method. It comes naturally when we have two points, we connect them with a straight line to fill out the missing information in between.
Pandas DataFrame fillna() MethodThe fillna() method replaces the NULL values with a specified value. The fillna() method returns a new DataFrame object unless the inplace parameter is set to True , in that case the fillna() method does the replacing in the original DataFrame instead.
Thank-you Dan Allan. I'm afraid I don't have the reputation to comment but Dan Allan's interpolate function raises an exception if asked to interpolate a time already defined in the ts index. E.g.
s = pd.to_datetime('2015-08-26 00:00:00')
e = pd.to_datetime('2015-08-26 00:10:00')
ts=pd.Series([0,8000],index=[s,e])
interpolate(ts,pd.to_datetime('2015-08-26 00:00:00'))
My minor modification of the above is:
def interpolate(ts, target):
if target in ts.index:
return ts[target]
ts1 = ts.sort_index()
b = (ts1.index > target).argmax() # index of first entry after target
s = ts1.iloc[b-1:b+1]
# Insert empty value at target time.
s = s.reindex(pd.to_datetime(list(s.index.values) + [pd.to_datetime(target)]))
return s.interpolate(method='time').loc[target]
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