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atm the functions herein operate primarily on DataFrames, because they inherently bind timestamps/frequencies to the data samples, which are used in ~every function in this package. However, I personally have noticed some qualitative slowdowns in the test executions since converting from the ndarray API to the DataFrame API. This makes me think there might be some value in supporting both API's: the DataFrame one for convenience, and the ndarray one for better performance. (We could even support xarrays in a third API.)
The text was updated successfully, but these errors were encountered:
CrepeGoat
changed the title
support pandas.DataFrames and numpy.ndarrays as inputs?
support pandas.DataFrames *and* numpy.ndarrays as inputs?
Sep 8, 2021
CrepeGoat
changed the title
support pandas.DataFrames *and* numpy.ndarrays as inputs?
support pandas.DataFrames AND numpy.ndarrays as inputs?
Sep 8, 2021
atm the functions herein operate primarily on
DataFrame
s, because they inherently bind timestamps/frequencies to the data samples, which are used in ~every function in this package. However, I personally have noticed some qualitative slowdowns in the test executions since converting from thendarray
API to theDataFrame
API. This makes me think there might be some value in supporting both API's: theDataFrame
one for convenience, and thendarray
one for better performance. (We could even supportxarray
s in a third API.)The text was updated successfully, but these errors were encountered: