distributed_upsample_operator¶
Operator to upsample a timeseries based dataframe in a distributed way
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class
tasrif.processing_pipeline.custom.distributed_upsample_operator.DistributedUpsampleOperator(rule)¶ Upsamples the dataframe based assuming the index
Example:
>>> import pandas as pd >>> from tasrif.processing_pipeline.custom import DistributedUpsampleOperator >>> df = pd.DataFrame([ >>> ["2020-05-01", 16.5], >>> ["2020-05-02", 19.1], >>> ['2020-05-03', 0]], >>> columns=['timestamp', 'sedentary_hours']) >>> >>> df['timestamp'] = pd.to_datetime(df['timestamp']) >>> df = df.set_index('timestamp') >>> op = DistributedUpsampleOperator('6h') >>> df = op.process(df) >>> [ sleep_level >>> timestamp >>> 2020-05-01 1.333333 >>> 2020-05-02 1.000000] [ sedentary_hours timestamp 2020-05-01 00:00:00 4.125 2020-05-01 06:00:00 4.125 2020-05-01 12:00:00 4.125 2020-05-01 18:00:00 4.125 2020-05-02 00:00:00 4.775 2020-05-02 06:00:00 4.775 2020-05-02 12:00:00 4.775 2020-05-02 18:00:00 4.775 2020-05-03 00:00:00 0.000]
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__init__(rule)¶ Creates a new instance of ResampleOperator
- Parameters
rule (ruleDateOffset, Timedelta, str) – The offset string or object representing target conversion.
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