tutorialto store some features in. Edit the
urlfield to specify a local file storage location that you would like to use. Feature values will be saved within this folder using parquet format.
valuecolumns to store:
tutorial/squaredthat contains the square of every value in
tutorial/number. To do this, define a transform as follows:
from_featuresand should return a series/dataframe of transformed timeseries values. We can now look at some of our timeseries data using:
load_dataframemethod, we can easily join, resample and filter the features. For example to get a monthly timeseries for 2020 we could run: