I am trying to create a function which resamples time series data in pandas. I would like to have the option to specify the type of aggregation that occurs depending on what type of data I am sending through (i.e. for some data, taking the sum of each bin is appropriate, while for others, taking the mean is needed, etc.). For example data like these:
import pandas as pd
import numpy as np
dr = pd.date_range('01-01-2020', '01-03-2020', freq='1H')
df = pd.DataFrame(np.random.rand(len(dr)), index=dr)
I could have a function like this:
def process(df, freq='3H', method='sum'):
r = df.resample(freq)
if method == 'sum':
r = r.sum()
elif method == 'mean':
r = r.mean()
#...
#more options
#...
return r
For a small amount of aggregation methods, this is fine, but seems like it could be tedious if I wanted to select from all of the possible choices.
I was hoping to use getattr to implement something like this post (under "Putting it to work: generalizing method calls"). However, I can't find a way to do this:
def process2(df, freq='3H', method='sum'):
r = df.resample(freq)
foo = getattr(r, method)
return r.foo()
#fails with:
#AttributeError: 'DatetimeIndexResampler' object has no attribute 'foo'
def process3(df, freq='3H', method='sum'):
r = df.resample(freq)
foo = getattr(r, method)
return foo(r)
#fails with:
#TypeError: __init__() missing 1 required positional argument: 'obj'
I get why process2 fails (calling r.foo() looks for the method foo() of r, not the variable foo). But I don't think I get why process3 fails.
I know another approach would be to pass functions to the parameter method, and then apply those functions on r. My inclination is that this would be less efficient? And it still doesn't allow me to access the built-in Resample methods directly.
Is there a working, more concise way to achieve this? Thanks!
